Overview

Dataset statistics

Number of variables39
Number of observations54294
Missing cells281768
Missing cells (%)13.3%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory16.2 MiB
Average record size in memory312.0 B

Variable types

Categorical17
Numeric22

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
permalink has a high cardinality: 49436 distinct valuesHigh cardinality
name has a high cardinality: 49350 distinct valuesHigh cardinality
homepage_url has a high cardinality: 45850 distinct valuesHigh cardinality
category_list has a high cardinality: 16675 distinct valuesHigh cardinality
market has a high cardinality: 753 distinct valuesHigh cardinality
funding_total_usd has a high cardinality: 14617 distinct valuesHigh cardinality
country_code has a high cardinality: 115 distinct valuesHigh cardinality
state_code has a high cardinality: 61 distinct valuesHigh cardinality
region has a high cardinality: 1089 distinct valuesHigh cardinality
city has a high cardinality: 4188 distinct valuesHigh cardinality
founded_at has a high cardinality: 3369 distinct valuesHigh cardinality
founded_month has a high cardinality: 420 distinct valuesHigh cardinality
founded_quarter has a high cardinality: 218 distinct valuesHigh cardinality
first_funding_at has a high cardinality: 3914 distinct valuesHigh cardinality
last_funding_at has a high cardinality: 3657 distinct valuesHigh cardinality
venture is highly overall correlated with round_A and 2 other fieldsHigh correlation
round_A is highly overall correlated with ventureHigh correlation
round_B is highly overall correlated with ventureHigh correlation
round_E is highly overall correlated with round_HHigh correlation
round_G is highly overall correlated with round_HHigh correlation
round_H is highly overall correlated with venture and 2 other fieldsHigh correlation
status is highly imbalanced (56.6%)Imbalance
country_code is highly imbalanced (62.0%)Imbalance
round_H is highly imbalanced (99.9%)Imbalance
permalink has 4856 (8.9%) missing valuesMissing
name has 4857 (8.9%) missing valuesMissing
homepage_url has 8305 (15.3%) missing valuesMissing
category_list has 8817 (16.2%) missing valuesMissing
market has 8824 (16.3%) missing valuesMissing
funding_total_usd has 4856 (8.9%) missing valuesMissing
status has 6170 (11.4%) missing valuesMissing
country_code has 10129 (18.7%) missing valuesMissing
state_code has 24133 (44.4%) missing valuesMissing
region has 10129 (18.7%) missing valuesMissing
city has 10972 (20.2%) missing valuesMissing
funding_rounds has 4856 (8.9%) missing valuesMissing
founded_at has 15740 (29.0%) missing valuesMissing
founded_month has 15812 (29.1%) missing valuesMissing
founded_quarter has 15812 (29.1%) missing valuesMissing
founded_year has 15812 (29.1%) missing valuesMissing
first_funding_at has 4856 (8.9%) missing valuesMissing
last_funding_at has 4856 (8.9%) missing valuesMissing
seed has 4856 (8.9%) missing valuesMissing
venture has 4856 (8.9%) missing valuesMissing
equity_crowdfunding has 4856 (8.9%) missing valuesMissing
undisclosed has 4856 (8.9%) missing valuesMissing
convertible_note has 4856 (8.9%) missing valuesMissing
debt_financing has 4856 (8.9%) missing valuesMissing
angel has 4856 (8.9%) missing valuesMissing
grant has 4856 (8.9%) missing valuesMissing
private_equity has 4856 (8.9%) missing valuesMissing
post_ipo_equity has 4856 (8.9%) missing valuesMissing
post_ipo_debt has 4856 (8.9%) missing valuesMissing
secondary_market has 4856 (8.9%) missing valuesMissing
product_crowdfunding has 4856 (8.9%) missing valuesMissing
round_A has 4856 (8.9%) missing valuesMissing
round_B has 4856 (8.9%) missing valuesMissing
round_C has 4856 (8.9%) missing valuesMissing
round_D has 4856 (8.9%) missing valuesMissing
round_E has 4856 (8.9%) missing valuesMissing
round_F has 4856 (8.9%) missing valuesMissing
round_G has 4856 (8.9%) missing valuesMissing
round_H has 4856 (8.9%) missing valuesMissing
seed is highly skewed (γ1 = 61.54157134)Skewed
venture is highly skewed (γ1 = 24.67599225)Skewed
equity_crowdfunding is highly skewed (γ1 = 73.80129708)Skewed
undisclosed is highly skewed (γ1 = 57.58958109)Skewed
convertible_note is highly skewed (γ1 = 188.858663)Skewed
debt_financing is highly skewed (γ1 = 209.060153)Skewed
angel is highly skewed (γ1 = 42.16928955)Skewed
grant is highly skewed (γ1 = 83.31824864)Skewed
private_equity is highly skewed (γ1 = 51.55799352)Skewed
post_ipo_equity is highly skewed (γ1 = 122.8262149)Skewed
post_ipo_debt is highly skewed (γ1 = 128.6538519)Skewed
secondary_market is highly skewed (γ1 = 140.0189668)Skewed
product_crowdfunding is highly skewed (γ1 = 135.1912786)Skewed
round_B is highly skewed (γ1 = 20.44533729)Skewed
round_D is highly skewed (γ1 = 64.29255253)Skewed
round_E is highly skewed (γ1 = 32.90086749)Skewed
round_F is highly skewed (γ1 = 109.2272619)Skewed
round_G is highly skewed (γ1 = 155.7271315)Skewed
permalink is uniformly distributedUniform
name is uniformly distributedUniform
homepage_url is uniformly distributedUniform
seed has 35598 (65.6%) zerosZeros
venture has 26161 (48.2%) zerosZeros
equity_crowdfunding has 48916 (90.1%) zerosZeros
undisclosed has 48486 (89.3%) zerosZeros
convertible_note has 48881 (90.0%) zerosZeros
debt_financing has 45213 (83.3%) zerosZeros
angel has 46309 (85.3%) zerosZeros
grant has 48296 (89.0%) zerosZeros
private_equity has 48065 (88.5%) zerosZeros
post_ipo_equity has 49122 (90.5%) zerosZeros
post_ipo_debt has 49363 (90.9%) zerosZeros
secondary_market has 49419 (91.0%) zerosZeros
product_crowdfunding has 49225 (90.7%) zerosZeros
round_A has 40435 (74.5%) zerosZeros
round_B has 43991 (81.0%) zerosZeros
round_C has 46601 (85.8%) zerosZeros
round_D has 48150 (88.7%) zerosZeros
round_E has 48922 (90.1%) zerosZeros
round_F has 49266 (90.7%) zerosZeros
round_G has 49404 (91.0%) zerosZeros

Reproduction

Analysis started2023-07-04 06:27:57.951087
Analysis finished2023-07-04 06:30:08.462352
Duration2 minutes and 10.51 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

permalink
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct49436
Distinct (%)> 99.9%
Missing4856
Missing (%)8.9%
Memory size424.3 KiB
/organization/treasure-valley-urology-services
 
2
/organization/prysm
 
2
/organization/polyera
 
1
/organization/pollground
 
1
/organization/pollitoingles
 
1
Other values (49431)
49431 

Length

Max length99
Median length73
Mean length26.128767
Min length15

Characters and Unicode

Total characters1291754
Distinct characters45
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49434 ?
Unique (%)> 99.9%

Sample

1st row/organization/waywire
2nd row/organization/tv-communications
3rd row/organization/rock-your-paper
4th row/organization/in-touch-network
5th row/organization/r-ranch-and-mine

Common Values

ValueCountFrequency (%)
/organization/treasure-valley-urology-services 2
 
< 0.1%
/organization/prysm 2
 
< 0.1%
/organization/polyera 1
 
< 0.1%
/organization/pollground 1
 
< 0.1%
/organization/pollitoingles 1
 
< 0.1%
/organization/pollsb 1
 
< 0.1%
/organization/pollvaultr 1
 
< 0.1%
/organization/polwire 1
 
< 0.1%
/organization/poly-adaptive 1
 
< 0.1%
/organization/polyactiva 1
 
< 0.1%
Other values (49426) 49426
91.0%
(Missing) 4856
 
8.9%

Length

2023-07-04T09:30:08.748535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
organization/treasure-valley-urology-services 2
 
< 0.1%
organization/prysm 2
 
< 0.1%
organization/axon-ghost-sentinel 1
 
< 0.1%
organization/20-20-gene-systems-inc 1
 
< 0.1%
organization/1248 1
 
< 0.1%
organization/1-4-all 1
 
< 0.1%
organization/rock-your-paper 1
 
< 0.1%
organization/in-touch-network 1
 
< 0.1%
organization/r-ranch-and-mine 1
 
< 0.1%
organization/club-domains 1
 
< 0.1%
Other values (49426) 49426
> 99.9%

Most occurring characters

ValueCountFrequency (%)
a 145698
11.3%
i 145429
11.3%
o 144257
11.2%
n 136104
10.5%
/ 98876
 
7.7%
t 89611
 
6.9%
r 85489
 
6.6%
g 64151
 
5.0%
e 61255
 
4.7%
z 51849
 
4.0%
Other values (35) 269035
20.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1157292
89.6%
Other Punctuation 98876
 
7.7%
Dash Punctuation 32900
 
2.5%
Decimal Number 2675
 
0.2%
Control 7
 
< 0.1%
Math Symbol 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 145698
12.6%
i 145429
12.6%
o 144257
12.5%
n 136104
11.8%
t 89611
7.7%
r 85489
7.4%
g 64151
 
5.5%
e 61255
 
5.3%
z 51849
 
4.5%
s 36992
 
3.2%
Other values (17) 196457
17.0%
Decimal Number
ValueCountFrequency (%)
2 839
31.4%
3 371
13.9%
1 319
 
11.9%
0 278
 
10.4%
4 248
 
9.3%
5 154
 
5.8%
6 136
 
5.1%
8 123
 
4.6%
9 123
 
4.6%
7 84
 
3.1%
Control
ValueCountFrequency (%)
™ 4
57.1%
’ 3
42.9%
Other Punctuation
ValueCountFrequency (%)
/ 98876
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32900
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1157292
89.6%
Common 134462
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 145698
12.6%
i 145429
12.6%
o 144257
12.5%
n 136104
11.8%
t 89611
7.7%
r 85489
7.4%
g 64151
 
5.5%
e 61255
 
5.3%
z 51849
 
4.5%
s 36992
 
3.2%
Other values (17) 196457
17.0%
Common
ValueCountFrequency (%)
/ 98876
73.5%
- 32900
 
24.5%
2 839
 
0.6%
3 371
 
0.3%
1 319
 
0.2%
0 278
 
0.2%
4 248
 
0.2%
5 154
 
0.1%
6 136
 
0.1%
8 123
 
0.1%
Other values (8) 218
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1291745
> 99.9%
None 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 145698
11.3%
i 145429
11.3%
o 144257
11.2%
n 136104
10.5%
/ 98876
 
7.7%
t 89611
 
6.9%
r 85489
 
6.6%
g 64151
 
5.0%
e 61255
 
4.7%
z 51849
 
4.0%
Other values (31) 269026
20.8%
None
ValueCountFrequency (%)
™ 4
44.4%
’ 3
33.3%
° 1
 
11.1%
é 1
 
11.1%

name
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct49350
Distinct (%)99.8%
Missing4857
Missing (%)8.9%
Memory size424.3 KiB
Roost
 
4
Spire
 
4
Cue
 
3
Hubbub
 
3
Roadmap
 
3
Other values (49345)
49420 

Length

Max length72
Median length57
Mean length12.112669
Min length1

Characters and Unicode

Total characters598814
Distinct characters143
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49273 ?
Unique (%)99.7%

Sample

1st row#waywire
2nd row&TV Communications
3rd row'Rock' Your Paper
4th row(In)Touch Network
5th row-R- Ranch and Mine

Common Values

ValueCountFrequency (%)
Roost 4
 
< 0.1%
Spire 4
 
< 0.1%
Cue 3
 
< 0.1%
Hubbub 3
 
< 0.1%
Roadmap 3
 
< 0.1%
Shift 3
 
< 0.1%
Compass 3
 
< 0.1%
Peach 3
 
< 0.1%
Nourish 2
 
< 0.1%
Lockbox 2
 
< 0.1%
Other values (49340) 49407
91.0%
(Missing) 4857
 
8.9%

Length

2023-07-04T09:30:09.068150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
technologies 1151
 
1.5%
systems 724
 
0.9%
inc 712
 
0.9%
solutions 618
 
0.8%
media 549
 
0.7%
group 535
 
0.7%
technology 509
 
0.6%
medical 486
 
0.6%
the 445
 
0.6%
software 391
 
0.5%
Other values (45575) 72955
92.3%

Most occurring characters

ValueCountFrequency (%)
e 56615
 
9.5%
o 41816
 
7.0%
i 40876
 
6.8%
a 40088
 
6.7%
n 33189
 
5.5%
t 32066
 
5.4%
r 31971
 
5.3%
29635
 
4.9%
s 25227
 
4.2%
l 23535
 
3.9%
Other values (133) 243796
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 456851
76.3%
Uppercase Letter 104942
 
17.5%
Space Separator 29637
 
4.9%
Other Punctuation 3821
 
0.6%
Decimal Number 2262
 
0.4%
Dash Punctuation 681
 
0.1%
Open Punctuation 242
 
< 0.1%
Close Punctuation 241
 
< 0.1%
Control 49
 
< 0.1%
Math Symbol 42
 
< 0.1%
Other values (7) 46
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 56615
12.4%
o 41816
 
9.2%
i 40876
 
8.9%
a 40088
 
8.8%
n 33189
 
7.3%
t 32066
 
7.0%
r 31971
 
7.0%
s 25227
 
5.5%
l 23535
 
5.2%
c 20398
 
4.5%
Other values (35) 111070
24.3%
Uppercase Letter
ValueCountFrequency (%)
S 11798
 
11.2%
C 8497
 
8.1%
T 7899
 
7.5%
M 7002
 
6.7%
A 6669
 
6.4%
P 6446
 
6.1%
I 5459
 
5.2%
B 4979
 
4.7%
E 4597
 
4.4%
L 4403
 
4.2%
Other values (23) 37193
35.4%
Other Punctuation
ValueCountFrequency (%)
. 2665
69.7%
, 506
 
13.2%
& 283
 
7.4%
' 158
 
4.1%
! 64
 
1.7%
/ 52
 
1.4%
? 51
 
1.3%
: 16
 
0.4%
@ 8
 
0.2%
* 7
 
0.2%
Other values (6) 11
 
0.3%
Decimal Number
ValueCountFrequency (%)
2 464
20.5%
3 333
14.7%
1 329
14.5%
0 275
12.2%
4 235
10.4%
5 151
 
6.7%
6 141
 
6.2%
9 126
 
5.6%
8 124
 
5.5%
7 84
 
3.7%
Control
ValueCountFrequency (%)
’ 20
40.8%
™ 18
36.7%
€ 3
 
6.1%
„ 2
 
4.1%
• 1
 
2.0%
‘ 1
 
2.0%
… 1
 
2.0%
‹ 1
 
2.0%
– 1
 
2.0%
 1
 
2.0%
Math Symbol
ValueCountFrequency (%)
+ 25
59.5%
| 14
33.3%
± 1
 
2.4%
> 1
 
2.4%
~ 1
 
2.4%
Other Number
ValueCountFrequency (%)
² 4
50.0%
¼ 1
 
12.5%
³ 1
 
12.5%
½ 1
 
12.5%
¾ 1
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 236
97.5%
[ 5
 
2.1%
{ 1
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 236
97.9%
] 4
 
1.7%
} 1
 
0.4%
Other Symbol
ValueCountFrequency (%)
® 10
52.6%
© 6
31.6%
° 3
 
15.8%
Currency Symbol
ValueCountFrequency (%)
¢ 3
50.0%
$ 2
33.3%
£ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
29635
> 99.9%
  2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 681
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Format
ValueCountFrequency (%)
­ 3
100.0%
Other Letter
ValueCountFrequency (%)
º 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 561794
93.8%
Common 37020
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 56615
 
10.1%
o 41816
 
7.4%
i 40876
 
7.3%
a 40088
 
7.1%
n 33189
 
5.9%
t 32066
 
5.7%
r 31971
 
5.7%
s 25227
 
4.5%
l 23535
 
4.2%
c 20398
 
3.6%
Other values (68) 216013
38.5%
Common
ValueCountFrequency (%)
29635
80.1%
. 2665
 
7.2%
- 681
 
1.8%
, 506
 
1.4%
2 464
 
1.3%
3 333
 
0.9%
1 329
 
0.9%
& 283
 
0.8%
0 275
 
0.7%
( 236
 
0.6%
Other values (55) 1613
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 598594
> 99.9%
None 220
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 56615
 
9.5%
o 41816
 
7.0%
i 40876
 
6.8%
a 40088
 
6.7%
n 33189
 
5.5%
t 32066
 
5.4%
r 31971
 
5.3%
29635
 
5.0%
s 25227
 
4.2%
l 23535
 
3.9%
Other values (80) 243576
40.7%
None
ValueCountFrequency (%)
é 22
 
10.0%
’ 20
 
9.1%
™ 18
 
8.2%
à 17
 
7.7%
á 16
 
7.3%
í 10
 
4.5%
® 10
 
4.5%
ó 8
 
3.6%
ü 7
 
3.2%
© 6
 
2.7%
Other values (43) 86
39.1%

homepage_url
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct45850
Distinct (%)99.7%
Missing8305
Missing (%)15.3%
Memory size424.3 KiB
http://spaceport.io
 
2
http://ivillage.com
 
2
http://www.kuwo.cn
 
2
http://gui.de
 
2
http://primordialgenetics.com
 
2
Other values (45845)
45979 

Length

Max length224
Median length114
Mean length23.615952
Min length11

Characters and Unicode

Total characters1086074
Distinct characters79
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45711 ?
Unique (%)99.4%

Sample

1st rowhttp://www.waywire.com
2nd rowhttp://enjoyandtv.com
3rd rowhttp://www.rockyourpaper.org
4th rowhttp://www.InTouchNetwork.com
5th rowhttp://nic.club/

Common Values

ValueCountFrequency (%)
http://spaceport.io 2
 
< 0.1%
http://ivillage.com 2
 
< 0.1%
http://www.kuwo.cn 2
 
< 0.1%
http://gui.de 2
 
< 0.1%
http://primordialgenetics.com 2
 
< 0.1%
http://www.myworldwall.com 2
 
< 0.1%
http://evetab.com/ 2
 
< 0.1%
http://www.cirqle.nl/ 2
 
< 0.1%
http://www.appvance.com 2
 
< 0.1%
http://domino.com 2
 
< 0.1%
Other values (45840) 45969
84.7%
(Missing) 8305
 
15.3%

Length

2023-07-04T09:30:09.297903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http://www.viewpointcs.com 3
 
< 0.1%
http://spaceport.io 2
 
< 0.1%
http://www.smore.com 2
 
< 0.1%
http://www.inmarket.com 2
 
< 0.1%
http://gainfitness.com 2
 
< 0.1%
http://bubbly.net 2
 
< 0.1%
http://www.instoreaudionetwork.com 2
 
< 0.1%
http://www.dachisgroup.com 2
 
< 0.1%
http://rang.com 2
 
< 0.1%
http://www.voolks.com 2
 
< 0.1%
Other values (45818) 45995
> 99.9%

Most occurring characters

ValueCountFrequency (%)
t 124027
 
11.4%
/ 97972
 
9.0%
w 96842
 
8.9%
. 78683
 
7.2%
o 75131
 
6.9%
c 61949
 
5.7%
p 61412
 
5.7%
h 57296
 
5.3%
m 54761
 
5.0%
e 50507
 
4.7%
Other values (69) 327494
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 854459
78.7%
Other Punctuation 222847
 
20.5%
Decimal Number 3555
 
0.3%
Uppercase Letter 2546
 
0.2%
Dash Punctuation 2464
 
0.2%
Connector Punctuation 96
 
< 0.1%
Math Symbol 80
 
< 0.1%
Space Separator 27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 124027
14.5%
w 96842
11.3%
o 75131
 
8.8%
c 61949
 
7.3%
p 61412
 
7.2%
h 57296
 
6.7%
m 54761
 
6.4%
e 50507
 
5.9%
a 38514
 
4.5%
i 36459
 
4.3%
Other values (16) 197561
23.1%
Uppercase Letter
ValueCountFrequency (%)
S 247
 
9.7%
C 195
 
7.7%
T 169
 
6.6%
A 153
 
6.0%
M 153
 
6.0%
P 144
 
5.7%
B 126
 
4.9%
L 126
 
4.9%
E 120
 
4.7%
G 118
 
4.6%
Other values (16) 995
39.1%
Other Punctuation
ValueCountFrequency (%)
/ 97972
44.0%
. 78683
35.3%
: 46000
20.6%
? 78
 
< 0.1%
% 33
 
< 0.1%
# 29
 
< 0.1%
& 28
 
< 0.1%
! 15
 
< 0.1%
, 5
 
< 0.1%
; 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 681
19.2%
1 579
16.3%
0 577
16.2%
3 441
12.4%
4 297
8.4%
5 228
 
6.4%
6 205
 
5.8%
8 204
 
5.7%
9 193
 
5.4%
7 150
 
4.2%
Math Symbol
ValueCountFrequency (%)
= 79
98.8%
+ 1
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 2464
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 96
100.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 857005
78.9%
Common 229069
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 124027
14.5%
w 96842
11.3%
o 75131
 
8.8%
c 61949
 
7.2%
p 61412
 
7.2%
h 57296
 
6.7%
m 54761
 
6.4%
e 50507
 
5.9%
a 38514
 
4.5%
i 36459
 
4.3%
Other values (42) 200107
23.3%
Common
ValueCountFrequency (%)
/ 97972
42.8%
. 78683
34.3%
: 46000
20.1%
- 2464
 
1.1%
2 681
 
0.3%
1 579
 
0.3%
0 577
 
0.3%
3 441
 
0.2%
4 297
 
0.1%
5 228
 
0.1%
Other values (17) 1147
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1086074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 124027
 
11.4%
/ 97972
 
9.0%
w 96842
 
8.9%
. 78683
 
7.2%
o 75131
 
6.9%
c 61949
 
5.7%
p 61412
 
5.7%
h 57296
 
5.3%
m 54761
 
5.0%
e 50507
 
4.7%
Other values (69) 327494
30.2%

category_list
Categorical

HIGH CARDINALITY  MISSING 

Distinct16675
Distinct (%)36.7%
Missing8817
Missing (%)16.2%
Memory size424.3 KiB
|Software|
3650 
|Biotechnology|
3597 
|E-Commerce|
 
1263
|Mobile|
 
1211
|Curated Web|
 
1120
Other values (16670)
34636 

Length

Max length255
Median length202
Mean length27.464696
Min length4

Characters and Unicode

Total characters1249012
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15373 ?
Unique (%)33.8%

Sample

1st row|Entertainment|Politics|Social Media|News|
2nd row|Games|
3rd row|Publishing|Education|
4th row|Electronics|Guides|Coffee|Restaurants|Music|iPhone|Apps|Mobile|iOS|E-Commerce|
5th row|Tourism|Entertainment|Games|

Common Values

ValueCountFrequency (%)
|Software| 3650
 
6.7%
|Biotechnology| 3597
 
6.6%
|E-Commerce| 1263
 
2.3%
|Mobile| 1211
 
2.2%
|Curated Web| 1120
 
2.1%
|Clean Technology| 1109
 
2.0%
|Hardware + Software| 968
 
1.8%
|Enterprise Software| 888
 
1.6%
|Health Care| 853
 
1.6%
|Games| 846
 
1.6%
Other values (16665) 29972
55.2%
(Missing) 8817
 
16.2%

Length

2023-07-04T09:30:09.832401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
software 7059
 
7.9%
biotechnology 3597
 
4.0%
web 3547
 
4.0%
and 2615
 
2.9%
health 2213
 
2.5%
mobile 1603
 
1.8%
1583
 
1.8%
technology 1564
 
1.7%
media 1528
 
1.7%
social 1463
 
1.6%
Other values (19610) 62922
70.2%

Most occurring characters

ValueCountFrequency (%)
| 145007
 
11.6%
e 121176
 
9.7%
a 84156
 
6.7%
o 80602
 
6.5%
i 79148
 
6.3%
t 73964
 
5.9%
n 70827
 
5.7%
r 63049
 
5.0%
l 46970
 
3.8%
s 46757
 
3.7%
Other values (53) 437356
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 906621
72.6%
Uppercase Letter 147242
 
11.8%
Math Symbol 146421
 
11.7%
Space Separator 44217
 
3.5%
Dash Punctuation 3709
 
0.3%
Decimal Number 578
 
< 0.1%
Other Punctuation 224
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 121176
13.4%
a 84156
9.3%
o 80602
8.9%
i 79148
 
8.7%
t 73964
 
8.2%
n 70827
 
7.8%
r 63049
 
7.0%
l 46970
 
5.2%
s 46757
 
5.2%
c 39722
 
4.4%
Other values (16) 200250
22.1%
Uppercase Letter
ValueCountFrequency (%)
S 28063
19.1%
M 17620
12.0%
C 17494
11.9%
E 11293
 
7.7%
A 8351
 
5.7%
B 7878
 
5.4%
H 7035
 
4.8%
T 6860
 
4.7%
W 6479
 
4.4%
P 6184
 
4.2%
Other values (15) 29985
20.4%
Decimal Number
ValueCountFrequency (%)
2 369
63.8%
3 135
 
23.4%
1 37
 
6.4%
7 19
 
3.3%
0 18
 
3.1%
Other Punctuation
ValueCountFrequency (%)
& 194
86.6%
. 18
 
8.0%
/ 12
 
5.4%
Math Symbol
ValueCountFrequency (%)
| 145007
99.0%
+ 1414
 
1.0%
Space Separator
ValueCountFrequency (%)
44217
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3709
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1053863
84.4%
Common 195149
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 121176
 
11.5%
a 84156
 
8.0%
o 80602
 
7.6%
i 79148
 
7.5%
t 73964
 
7.0%
n 70827
 
6.7%
r 63049
 
6.0%
l 46970
 
4.5%
s 46757
 
4.4%
c 39722
 
3.8%
Other values (41) 347492
33.0%
Common
ValueCountFrequency (%)
| 145007
74.3%
44217
 
22.7%
- 3709
 
1.9%
+ 1414
 
0.7%
2 369
 
0.2%
& 194
 
0.1%
3 135
 
0.1%
1 37
 
< 0.1%
7 19
 
< 0.1%
. 18
 
< 0.1%
Other values (2) 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1249012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
| 145007
 
11.6%
e 121176
 
9.7%
a 84156
 
6.7%
o 80602
 
6.5%
i 79148
 
6.3%
t 73964
 
5.9%
n 70827
 
5.7%
r 63049
 
5.0%
l 46970
 
3.8%
s 46757
 
3.7%
Other values (53) 437356
35.0%

market
Categorical

HIGH CARDINALITY  MISSING 

Distinct753
Distinct (%)1.7%
Missing8824
Missing (%)16.3%
Memory size424.3 KiB
Software
4620 
Biotechnology
3688 
Mobile
 
1983
E-Commerce
 
1805
Curated Web
 
1655
Other values (748)
31719 

Length

Max length39
Median length31
Mean length13.197339
Min length4

Characters and Unicode

Total characters600083
Distinct characters61
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)0.2%

Sample

1st row News
2nd row Games
3rd row Publishing
4th row Electronics
5th row Tourism

Common Values

ValueCountFrequency (%)
Software 4620
 
8.5%
Biotechnology 3688
 
6.8%
Mobile 1983
 
3.7%
E-Commerce 1805
 
3.3%
Curated Web 1655
 
3.0%
Enterprise Software 1280
 
2.4%
Health Care 1207
 
2.2%
Clean Technology 1200
 
2.2%
Games 1182
 
2.2%
Hardware + Software 1081
 
2.0%
Other values (743) 25769
47.5%
(Missing) 8824
 
16.3%

Length

2023-07-04T09:30:10.081219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
software 7058
 
10.9%
biotechnology 3689
 
5.7%
mobile 2329
 
3.6%
web 2310
 
3.6%
health 2233
 
3.5%
e-commerce 1827
 
2.8%
technology 1701
 
2.6%
curated 1655
 
2.6%
social 1377
 
2.1%
media 1364
 
2.1%
Other values (715) 38991
60.4%

Most occurring characters

ValueCountFrequency (%)
110004
18.3%
e 56256
 
9.4%
o 41472
 
6.9%
a 38702
 
6.4%
t 35232
 
5.9%
i 32370
 
5.4%
n 31388
 
5.2%
r 29834
 
5.0%
l 22630
 
3.8%
c 19382
 
3.2%
Other values (51) 182813
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 421896
70.3%
Space Separator 110004
 
18.3%
Uppercase Letter 64911
 
10.8%
Dash Punctuation 1949
 
0.3%
Math Symbol 1099
 
0.2%
Decimal Number 152
 
< 0.1%
Other Punctuation 72
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 56256
13.3%
o 41472
9.8%
a 38702
9.2%
t 35232
 
8.4%
i 32370
 
7.7%
n 31388
 
7.4%
r 29834
 
7.1%
l 22630
 
5.4%
c 19382
 
4.6%
s 19360
 
4.6%
Other values (16) 95270
22.6%
Uppercase Letter
ValueCountFrequency (%)
S 12706
19.6%
C 8451
13.0%
M 6783
10.4%
E 5494
8.5%
B 4772
 
7.4%
H 4584
 
7.1%
W 3427
 
5.3%
T 3194
 
4.9%
A 3053
 
4.7%
P 1954
 
3.0%
Other values (14) 10493
16.2%
Decimal Number
ValueCountFrequency (%)
2 86
56.6%
3 42
27.6%
1 19
 
12.5%
7 4
 
2.6%
0 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
& 67
93.1%
/ 4
 
5.6%
. 1
 
1.4%
Space Separator
ValueCountFrequency (%)
110004
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1949
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 486807
81.1%
Common 113276
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 56256
 
11.6%
o 41472
 
8.5%
a 38702
 
8.0%
t 35232
 
7.2%
i 32370
 
6.6%
n 31388
 
6.4%
r 29834
 
6.1%
l 22630
 
4.6%
c 19382
 
4.0%
s 19360
 
4.0%
Other values (40) 160181
32.9%
Common
ValueCountFrequency (%)
110004
97.1%
- 1949
 
1.7%
+ 1099
 
1.0%
2 86
 
0.1%
& 67
 
0.1%
3 42
 
< 0.1%
1 19
 
< 0.1%
7 4
 
< 0.1%
/ 4
 
< 0.1%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110004
18.3%
e 56256
 
9.4%
o 41472
 
6.9%
a 38702
 
6.4%
t 35232
 
5.9%
i 32370
 
5.4%
n 31388
 
5.2%
r 29834
 
5.0%
l 22630
 
3.8%
c 19382
 
3.2%
Other values (51) 182813
30.5%

funding_total_usd
Categorical

HIGH CARDINALITY  MISSING 

Distinct14617
Distinct (%)29.6%
Missing4856
Missing (%)8.9%
Memory size424.3 KiB
-
8531 
10,00,000
 
925
5,00,000
 
761
1,00,000
 
749
40,000
 
680
Other values (14612)
37792 

Length

Max length17
Median length16
Mean length9.8788381
Min length3

Characters and Unicode

Total characters488390
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12621 ?
Unique (%)25.5%

Sample

1st row 17,50,000
2nd row 40,00,000
3rd row 40,000
4th row 15,00,000
5th row 60,000

Common Values

ValueCountFrequency (%)
- 8531
 
15.7%
10,00,000 925
 
1.7%
5,00,000 761
 
1.4%
1,00,000 749
 
1.4%
40,000 680
 
1.3%
20,00,000 623
 
1.1%
50,000 560
 
1.0%
2,50,000 526
 
1.0%
1,00,00,000 489
 
0.9%
50,00,000 466
 
0.9%
Other values (14607) 35128
64.7%
(Missing) 4856
 
8.9%

Length

2023-07-04T09:30:10.284455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8531
 
17.3%
10,00,000 925
 
1.9%
5,00,000 761
 
1.5%
1,00,000 749
 
1.5%
40,000 680
 
1.4%
20,00,000 623
 
1.3%
50,000 560
 
1.1%
2,50,000 526
 
1.1%
1,00,00,000 489
 
1.0%
50,00,000 466
 
0.9%
Other values (14607) 35128
71.1%

Most occurring characters

ValueCountFrequency (%)
0 150596
30.8%
115938
23.7%
, 87045
17.8%
1 21518
 
4.4%
5 20788
 
4.3%
2 17761
 
3.6%
3 13130
 
2.7%
4 12223
 
2.5%
6 10679
 
2.2%
7 10556
 
2.2%
Other values (3) 28156
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 276876
56.7%
Space Separator 115938
23.7%
Other Punctuation 87045
 
17.8%
Dash Punctuation 8531
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 150596
54.4%
1 21518
 
7.8%
5 20788
 
7.5%
2 17761
 
6.4%
3 13130
 
4.7%
4 12223
 
4.4%
6 10679
 
3.9%
7 10556
 
3.8%
9 10100
 
3.6%
8 9525
 
3.4%
Space Separator
ValueCountFrequency (%)
115938
100.0%
Other Punctuation
ValueCountFrequency (%)
, 87045
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8531
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 488390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 150596
30.8%
115938
23.7%
, 87045
17.8%
1 21518
 
4.4%
5 20788
 
4.3%
2 17761
 
3.6%
3 13130
 
2.7%
4 12223
 
2.5%
6 10679
 
2.2%
7 10556
 
2.2%
Other values (3) 28156
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 488390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 150596
30.8%
115938
23.7%
, 87045
17.8%
1 21518
 
4.4%
5 20788
 
4.3%
2 17761
 
3.6%
3 13130
 
2.7%
4 12223
 
2.5%
6 10679
 
2.2%
7 10556
 
2.2%
Other values (3) 28156
 
5.8%

status
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing6170
Missing (%)11.4%
Memory size424.3 KiB
operating
41829 
acquired
 
3692
closed
 
2603

Length

Max length9
Median length9
Mean length8.7610132
Min length6

Characters and Unicode

Total characters421615
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacquired
2nd rowoperating
3rd rowoperating
4th rowoperating
5th rowoperating

Common Values

ValueCountFrequency (%)
operating 41829
77.0%
acquired 3692
 
6.8%
closed 2603
 
4.8%
(Missing) 6170
 
11.4%

Length

2023-07-04T09:30:10.479808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-04T09:30:10.634663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
operating 41829
86.9%
acquired 3692
 
7.7%
closed 2603
 
5.4%

Most occurring characters

ValueCountFrequency (%)
e 48124
11.4%
r 45521
10.8%
a 45521
10.8%
i 45521
10.8%
o 44432
10.5%
p 41829
9.9%
t 41829
9.9%
n 41829
9.9%
g 41829
9.9%
c 6295
 
1.5%
Other values (5) 18885
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 421615
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 48124
11.4%
r 45521
10.8%
a 45521
10.8%
i 45521
10.8%
o 44432
10.5%
p 41829
9.9%
t 41829
9.9%
n 41829
9.9%
g 41829
9.9%
c 6295
 
1.5%
Other values (5) 18885
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 421615
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 48124
11.4%
r 45521
10.8%
a 45521
10.8%
i 45521
10.8%
o 44432
10.5%
p 41829
9.9%
t 41829
9.9%
n 41829
9.9%
g 41829
9.9%
c 6295
 
1.5%
Other values (5) 18885
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 421615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 48124
11.4%
r 45521
10.8%
a 45521
10.8%
i 45521
10.8%
o 44432
10.5%
p 41829
9.9%
t 41829
9.9%
n 41829
9.9%
g 41829
9.9%
c 6295
 
1.5%
Other values (5) 18885
 
4.5%

country_code
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct115
Distinct (%)0.3%
Missing10129
Missing (%)18.7%
Memory size424.3 KiB
USA
28793 
GBR
 
2642
CAN
 
1405
CHN
 
1239
DEU
 
968
Other values (110)
9118 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters132495
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st rowUSA
2nd rowUSA
3rd rowEST
4th rowGBR
5th rowUSA

Common Values

ValueCountFrequency (%)
USA 28793
53.0%
GBR 2642
 
4.9%
CAN 1405
 
2.6%
CHN 1239
 
2.3%
DEU 968
 
1.8%
FRA 866
 
1.6%
IND 849
 
1.6%
ISR 682
 
1.3%
ESP 549
 
1.0%
RUS 368
 
0.7%
Other values (105) 5804
 
10.7%
(Missing) 10129
 
18.7%

Length

2023-07-04T09:30:10.778406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa 28793
65.2%
gbr 2642
 
6.0%
can 1405
 
3.2%
chn 1239
 
2.8%
deu 968
 
2.2%
fra 866
 
2.0%
ind 849
 
1.9%
isr 682
 
1.5%
esp 549
 
1.2%
rus 368
 
0.8%
Other values (105) 5804
 
13.1%

Most occurring characters

ValueCountFrequency (%)
A 32525
24.5%
S 31479
23.8%
U 30850
23.3%
R 6177
 
4.7%
N 4895
 
3.7%
G 3401
 
2.6%
C 3306
 
2.5%
B 3203
 
2.4%
E 2532
 
1.9%
D 2420
 
1.8%
Other values (15) 11707
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 132495
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 32525
24.5%
S 31479
23.8%
U 30850
23.3%
R 6177
 
4.7%
N 4895
 
3.7%
G 3401
 
2.6%
C 3306
 
2.5%
B 3203
 
2.4%
E 2532
 
1.9%
D 2420
 
1.8%
Other values (15) 11707
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 132495
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 32525
24.5%
S 31479
23.8%
U 30850
23.3%
R 6177
 
4.7%
N 4895
 
3.7%
G 3401
 
2.6%
C 3306
 
2.5%
B 3203
 
2.4%
E 2532
 
1.9%
D 2420
 
1.8%
Other values (15) 11707
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 32525
24.5%
S 31479
23.8%
U 30850
23.3%
R 6177
 
4.7%
N 4895
 
3.7%
G 3401
 
2.6%
C 3306
 
2.5%
B 3203
 
2.4%
E 2532
 
1.9%
D 2420
 
1.8%
Other values (15) 11707
 
8.8%

state_code
Categorical

HIGH CARDINALITY  MISSING 

Distinct61
Distinct (%)0.2%
Missing24133
Missing (%)44.4%
Memory size424.3 KiB
CA
9917 
NY
2914 
MA
1969 
TX
1466 
WA
 
974
Other values (56)
12921 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters60322
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNY
2nd rowCA
3rd rowTX
4th rowFL
5th rowIL

Common Values

ValueCountFrequency (%)
CA 9917
18.3%
NY 2914
 
5.4%
MA 1969
 
3.6%
TX 1466
 
2.7%
WA 974
 
1.8%
FL 963
 
1.8%
IL 827
 
1.5%
PA 792
 
1.5%
CO 723
 
1.3%
ON 653
 
1.2%
Other values (51) 8963
 
16.5%
(Missing) 24133
44.4%

Length

2023-07-04T09:30:10.950934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 9917
32.9%
ny 2914
 
9.7%
ma 1969
 
6.5%
tx 1466
 
4.9%
wa 974
 
3.2%
fl 963
 
3.2%
il 827
 
2.7%
pa 792
 
2.6%
co 723
 
2.4%
on 653
 
2.2%
Other values (51) 8963
29.7%

Most occurring characters

ValueCountFrequency (%)
A 15638
25.9%
C 12276
20.4%
N 6163
 
10.2%
M 3552
 
5.9%
Y 3044
 
5.0%
T 2636
 
4.4%
O 2516
 
4.2%
L 1993
 
3.3%
I 1856
 
3.1%
X 1466
 
2.4%
Other values (16) 9182
15.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60322
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15638
25.9%
C 12276
20.4%
N 6163
 
10.2%
M 3552
 
5.9%
Y 3044
 
5.0%
T 2636
 
4.4%
O 2516
 
4.2%
L 1993
 
3.3%
I 1856
 
3.1%
X 1466
 
2.4%
Other values (16) 9182
15.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 60322
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15638
25.9%
C 12276
20.4%
N 6163
 
10.2%
M 3552
 
5.9%
Y 3044
 
5.0%
T 2636
 
4.4%
O 2516
 
4.2%
L 1993
 
3.3%
I 1856
 
3.1%
X 1466
 
2.4%
Other values (16) 9182
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15638
25.9%
C 12276
20.4%
N 6163
 
10.2%
M 3552
 
5.9%
Y 3044
 
5.0%
T 2636
 
4.4%
O 2516
 
4.2%
L 1993
 
3.3%
I 1856
 
3.1%
X 1466
 
2.4%
Other values (16) 9182
15.2%

region
Categorical

HIGH CARDINALITY  MISSING 

Distinct1089
Distinct (%)2.5%
Missing10129
Missing (%)18.7%
Memory size424.3 KiB
SF Bay Area
6804 
New York City
 
2577
Boston
 
1837
London
 
1588
Los Angeles
 
1389
Other values (1084)
29970 

Length

Max length38
Median length25
Mean length9.2863127
Min length3

Characters and Unicode

Total characters410130
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique259 ?
Unique (%)0.6%

Sample

1st rowNew York City
2nd rowLos Angeles
3rd rowTallinn
4th rowLondon
5th rowDallas

Common Values

ValueCountFrequency (%)
SF Bay Area 6804
 
12.5%
New York City 2577
 
4.7%
Boston 1837
 
3.4%
London 1588
 
2.9%
Los Angeles 1389
 
2.6%
Seattle 932
 
1.7%
Washington, D.C. 785
 
1.4%
Chicago 749
 
1.4%
San Diego 700
 
1.3%
Denver 636
 
1.2%
Other values (1079) 26168
48.2%
(Missing) 10129
 
18.7%

Length

2023-07-04T09:30:11.150683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bay 6824
 
8.8%
sf 6804
 
8.8%
area 6804
 
8.8%
city 3171
 
4.1%
3052
 
3.9%
new 2985
 
3.9%
other 2771
 
3.6%
york 2657
 
3.4%
boston 1839
 
2.4%
london 1588
 
2.1%
Other values (1195) 38787
50.2%

Most occurring characters

ValueCountFrequency (%)
a 37976
 
9.3%
e 34982
 
8.5%
33118
 
8.1%
o 26965
 
6.6%
n 24682
 
6.0%
r 23853
 
5.8%
i 20113
 
4.9%
t 19869
 
4.8%
l 14522
 
3.5%
s 13202
 
3.2%
Other values (55) 160848
39.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 283887
69.2%
Uppercase Letter 86297
 
21.0%
Space Separator 33118
 
8.1%
Other Punctuation 3624
 
0.9%
Dash Punctuation 3204
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37976
13.4%
e 34982
12.3%
o 26965
9.5%
n 24682
8.7%
r 23853
8.4%
i 20113
 
7.1%
t 19869
 
7.0%
l 14522
 
5.1%
s 13202
 
4.7%
y 11330
 
4.0%
Other values (22) 56393
19.9%
Uppercase Letter
ValueCountFrequency (%)
S 11939
13.8%
B 11760
13.6%
A 11716
13.6%
F 7513
8.7%
C 6939
8.0%
N 4869
 
5.6%
L 4651
 
5.4%
O 3946
 
4.6%
D 3861
 
4.5%
Y 2804
 
3.2%
Other values (18) 16299
18.9%
Other Punctuation
ValueCountFrequency (%)
. 1886
52.0%
, 1677
46.3%
' 61
 
1.7%
Space Separator
ValueCountFrequency (%)
33118
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 370184
90.3%
Common 39946
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37976
 
10.3%
e 34982
 
9.4%
o 26965
 
7.3%
n 24682
 
6.7%
r 23853
 
6.4%
i 20113
 
5.4%
t 19869
 
5.4%
l 14522
 
3.9%
s 13202
 
3.6%
S 11939
 
3.2%
Other values (50) 142081
38.4%
Common
ValueCountFrequency (%)
33118
82.9%
- 3204
 
8.0%
. 1886
 
4.7%
, 1677
 
4.2%
' 61
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 410053
> 99.9%
None 77
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 37976
 
9.3%
e 34982
 
8.5%
33118
 
8.1%
o 26965
 
6.6%
n 24682
 
6.0%
r 23853
 
5.8%
i 20113
 
4.9%
t 19869
 
4.8%
l 14522
 
3.5%
s 13202
 
3.2%
Other values (47) 160771
39.2%
None
ValueCountFrequency (%)
Ç 33
42.9%
á 20
26.0%
è 6
 
7.8%
ä 6
 
7.8%
ø 4
 
5.2%
É 3
 
3.9%
ú 3
 
3.9%
í 2
 
2.6%

city
Categorical

HIGH CARDINALITY  MISSING 

Distinct4188
Distinct (%)9.7%
Missing10972
Missing (%)20.2%
Memory size424.3 KiB
San Francisco
 
2615
New York
 
2334
London
 
1257
Palo Alto
 
597
Austin
 
583
Other values (4183)
35936 

Length

Max length32
Median length26
Mean length8.7052306
Min length2

Characters and Unicode

Total characters377128
Distinct characters87
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2113 ?
Unique (%)4.9%

Sample

1st rowNew York
2nd rowLos Angeles
3rd rowTallinn
4th rowLondon
5th rowFort Worth

Common Values

ValueCountFrequency (%)
San Francisco 2615
 
4.8%
New York 2334
 
4.3%
London 1257
 
2.3%
Palo Alto 597
 
1.1%
Austin 583
 
1.1%
Seattle 554
 
1.0%
Cambridge 554
 
1.0%
Chicago 514
 
0.9%
Los Angeles 508
 
0.9%
Mountain View 497
 
0.9%
Other values (4178) 33309
61.3%
(Missing) 10972
 
20.2%

Length

2023-07-04T09:30:11.377319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 4093
 
7.0%
francisco 2677
 
4.6%
new 2552
 
4.4%
york 2341
 
4.0%
london 1259
 
2.2%
city 765
 
1.3%
santa 702
 
1.2%
los 636
 
1.1%
alto 601
 
1.0%
palo 601
 
1.0%
Other values (4107) 42190
72.2%

Most occurring characters

ValueCountFrequency (%)
a 36535
 
9.7%
n 33523
 
8.9%
o 32757
 
8.7%
e 29403
 
7.8%
i 22869
 
6.1%
r 21240
 
5.6%
l 19433
 
5.2%
t 16797
 
4.5%
15095
 
4.0%
s 14981
 
4.0%
Other values (77) 134495
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 303198
80.4%
Uppercase Letter 58401
 
15.5%
Space Separator 15095
 
4.0%
Dash Punctuation 291
 
0.1%
Other Punctuation 110
 
< 0.1%
Modifier Symbol 30
 
< 0.1%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 36535
12.0%
n 33523
11.1%
o 32757
10.8%
e 29403
9.7%
i 22869
 
7.5%
r 21240
 
7.0%
l 19433
 
6.4%
t 16797
 
5.5%
s 14981
 
4.9%
c 10276
 
3.4%
Other values (37) 65384
21.6%
Uppercase Letter
ValueCountFrequency (%)
S 9233
15.8%
B 4806
 
8.2%
C 4643
 
8.0%
M 4222
 
7.2%
A 3861
 
6.6%
L 3749
 
6.4%
F 3553
 
6.1%
N 3509
 
6.0%
P 3461
 
5.9%
Y 2423
 
4.1%
Other values (22) 14941
25.6%
Decimal Number
ValueCountFrequency (%)
6 1
33.3%
4 1
33.3%
0 1
33.3%
Other Punctuation
ValueCountFrequency (%)
' 62
56.4%
. 48
43.6%
Space Separator
ValueCountFrequency (%)
15095
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 291
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 361599
95.9%
Common 15529
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 36535
 
10.1%
n 33523
 
9.3%
o 32757
 
9.1%
e 29403
 
8.1%
i 22869
 
6.3%
r 21240
 
5.9%
l 19433
 
5.4%
t 16797
 
4.6%
s 14981
 
4.1%
c 10276
 
2.8%
Other values (69) 123785
34.2%
Common
ValueCountFrequency (%)
15095
97.2%
- 291
 
1.9%
' 62
 
0.4%
. 48
 
0.3%
` 30
 
0.2%
6 1
 
< 0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376313
99.8%
None 815
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 36535
 
9.7%
n 33523
 
8.9%
o 32757
 
8.7%
e 29403
 
7.8%
i 22869
 
6.1%
r 21240
 
5.6%
l 19433
 
5.2%
t 16797
 
4.5%
15095
 
4.0%
s 14981
 
4.0%
Other values (50) 133680
35.5%
None
ValueCountFrequency (%)
é 177
21.7%
ã 141
17.3%
ü 118
14.5%
ö 83
10.2%
á 63
 
7.7%
ó 40
 
4.9%
Ç 33
 
4.0%
í 30
 
3.7%
ø 22
 
2.7%
è 21
 
2.6%
Other values (17) 87
10.7%

funding_rounds
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean1.6962053
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:11.585588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2942127
Coefficient of variation (CV)0.76300473
Kurtosis12.339926
Mean1.6962053
Median Absolute Deviation (MAD)0
Skewness2.9258481
Sum83857
Variance1.6749865
MonotonicityNot monotonic
2023-07-04T09:30:11.710936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 32039
59.0%
2 9219
 
17.0%
3 4026
 
7.4%
4 1997
 
3.7%
5 1001
 
1.8%
6 560
 
1.0%
7 252
 
0.5%
8 152
 
0.3%
9 84
 
0.2%
10 43
 
0.1%
Other values (7) 65
 
0.1%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
1 32039
59.0%
2 9219
 
17.0%
3 4026
 
7.4%
4 1997
 
3.7%
5 1001
 
1.8%
6 560
 
1.0%
7 252
 
0.5%
8 152
 
0.3%
9 84
 
0.2%
10 43
 
0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 1
 
< 0.1%
15 4
 
< 0.1%
14 4
 
< 0.1%
13 8
 
< 0.1%
12 12
 
< 0.1%
11 35
 
0.1%
10 43
 
0.1%
9 84
0.2%
8 152
0.3%

founded_at
Categorical

HIGH CARDINALITY  MISSING 

Distinct3369
Distinct (%)8.7%
Missing15740
Missing (%)29.0%
Memory size424.3 KiB
2012-01-01
 
2181
2011-01-01
 
2161
2010-01-01
 
1855
2009-01-01
 
1603
2013-01-01
 
1575
Other values (3364)
29179 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters385540
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1388 ?
Unique (%)3.6%

Sample

1st row2012-06-01
2nd row2012-10-26
3rd row2011-04-01
4th row2014-01-01
5th row2011-10-10

Common Values

ValueCountFrequency (%)
2012-01-01 2181
 
4.0%
2011-01-01 2161
 
4.0%
2010-01-01 1855
 
3.4%
2009-01-01 1603
 
3.0%
2013-01-01 1575
 
2.9%
2007-01-01 1364
 
2.5%
2008-01-01 1285
 
2.4%
2006-01-01 1138
 
2.1%
2005-01-01 1016
 
1.9%
2004-01-01 919
 
1.7%
Other values (3359) 23457
43.2%
(Missing) 15740
29.0%

Length

2023-07-04T09:30:11.870826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-01-01 2181
 
5.7%
2011-01-01 2161
 
5.6%
2010-01-01 1855
 
4.8%
2009-01-01 1603
 
4.2%
2013-01-01 1575
 
4.1%
2007-01-01 1364
 
3.5%
2008-01-01 1285
 
3.3%
2006-01-01 1138
 
3.0%
2005-01-01 1016
 
2.6%
2004-01-01 919
 
2.4%
Other values (3359) 23457
60.8%

Most occurring characters

ValueCountFrequency (%)
0 125347
32.5%
1 91377
23.7%
- 77108
20.0%
2 46357
 
12.0%
9 12365
 
3.2%
3 7938
 
2.1%
8 5712
 
1.5%
4 5168
 
1.3%
7 5078
 
1.3%
6 4562
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 308432
80.0%
Dash Punctuation 77108
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 125347
40.6%
1 91377
29.6%
2 46357
 
15.0%
9 12365
 
4.0%
3 7938
 
2.6%
8 5712
 
1.9%
4 5168
 
1.7%
7 5078
 
1.6%
6 4562
 
1.5%
5 4528
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 77108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 385540
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 125347
32.5%
1 91377
23.7%
- 77108
20.0%
2 46357
 
12.0%
9 12365
 
3.2%
3 7938
 
2.1%
8 5712
 
1.5%
4 5168
 
1.3%
7 5078
 
1.3%
6 4562
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 125347
32.5%
1 91377
23.7%
- 77108
20.0%
2 46357
 
12.0%
9 12365
 
3.2%
3 7938
 
2.1%
8 5712
 
1.5%
4 5168
 
1.3%
7 5078
 
1.3%
6 4562
 
1.2%

founded_month
Categorical

HIGH CARDINALITY  MISSING 

Distinct420
Distinct (%)1.1%
Missing15812
Missing (%)29.1%
Memory size424.3 KiB
2012-01
 
2327
2011-01
 
2286
2010-01
 
1952
2013-01
 
1722
2009-01
 
1655
Other values (415)
28540 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters269374
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)0.2%

Sample

1st row2012-06
2nd row2012-10
3rd row2011-04
4th row2014-01
5th row2011-10

Common Values

ValueCountFrequency (%)
2012-01 2327
 
4.3%
2011-01 2286
 
4.2%
2010-01 1952
 
3.6%
2013-01 1722
 
3.2%
2009-01 1655
 
3.0%
2007-01 1394
 
2.6%
2008-01 1336
 
2.5%
2006-01 1159
 
2.1%
2005-01 1026
 
1.9%
2004-01 933
 
1.7%
Other values (410) 22692
41.8%
(Missing) 15812
29.1%

Length

2023-07-04T09:30:12.006808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-01 2327
 
6.0%
2011-01 2286
 
5.9%
2010-01 1952
 
5.1%
2013-01 1722
 
4.5%
2009-01 1655
 
4.3%
2007-01 1394
 
3.6%
2008-01 1336
 
3.5%
2006-01 1159
 
3.0%
2005-01 1026
 
2.7%
2004-01 933
 
2.4%
Other values (410) 22692
59.0%

Most occurring characters

ValueCountFrequency (%)
0 91154
33.8%
1 56500
21.0%
2 43592
16.2%
- 38482
14.3%
9 11716
 
4.3%
3 6880
 
2.6%
8 4932
 
1.8%
4 4453
 
1.7%
7 4370
 
1.6%
6 3969
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 230892
85.7%
Dash Punctuation 38482
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91154
39.5%
1 56500
24.5%
2 43592
18.9%
9 11716
 
5.1%
3 6880
 
3.0%
8 4932
 
2.1%
4 4453
 
1.9%
7 4370
 
1.9%
6 3969
 
1.7%
5 3326
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 38482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 269374
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91154
33.8%
1 56500
21.0%
2 43592
16.2%
- 38482
14.3%
9 11716
 
4.3%
3 6880
 
2.6%
8 4932
 
1.8%
4 4453
 
1.7%
7 4370
 
1.6%
6 3969
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91154
33.8%
1 56500
21.0%
2 43592
16.2%
- 38482
14.3%
9 11716
 
4.3%
3 6880
 
2.6%
8 4932
 
1.8%
4 4453
 
1.7%
7 4370
 
1.6%
6 3969
 
1.5%

founded_quarter
Categorical

HIGH CARDINALITY  MISSING 

Distinct218
Distinct (%)0.6%
Missing15812
Missing (%)29.1%
Memory size424.3 KiB
2012-Q1
2904 
2011-Q1
2768 
2010-Q1
 
2259
2013-Q1
 
2206
2009-Q1
 
1852
Other values (213)
26493 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters269374
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)0.1%

Sample

1st row2012-Q2
2nd row2012-Q4
3rd row2011-Q2
4th row2014-Q1
5th row2011-Q4

Common Values

ValueCountFrequency (%)
2012-Q1 2904
 
5.3%
2011-Q1 2768
 
5.1%
2010-Q1 2259
 
4.2%
2013-Q1 2206
 
4.1%
2009-Q1 1852
 
3.4%
2007-Q1 1560
 
2.9%
2008-Q1 1523
 
2.8%
2006-Q1 1264
 
2.3%
2005-Q1 1099
 
2.0%
2004-Q1 965
 
1.8%
Other values (208) 20082
37.0%
(Missing) 15812
29.1%

Length

2023-07-04T09:30:12.150824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-q1 2904
 
7.5%
2011-q1 2768
 
7.2%
2010-q1 2259
 
5.9%
2013-q1 2206
 
5.7%
2009-q1 1852
 
4.8%
2007-q1 1560
 
4.1%
2008-q1 1523
 
4.0%
2006-q1 1264
 
3.3%
2005-q1 1099
 
2.9%
2004-q1 965
 
2.5%
Other values (208) 20082
52.2%

Most occurring characters

ValueCountFrequency (%)
0 55071
20.4%
1 54433
20.2%
2 45783
17.0%
- 38482
14.3%
Q 38482
14.3%
9 10210
 
3.8%
3 9558
 
3.5%
4 6772
 
2.5%
8 3530
 
1.3%
7 2966
 
1.1%
Other values (2) 4087
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 192410
71.4%
Dash Punctuation 38482
 
14.3%
Uppercase Letter 38482
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55071
28.6%
1 54433
28.3%
2 45783
23.8%
9 10210
 
5.3%
3 9558
 
5.0%
4 6772
 
3.5%
8 3530
 
1.8%
7 2966
 
1.5%
6 2303
 
1.2%
5 1784
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 38482
100.0%
Uppercase Letter
ValueCountFrequency (%)
Q 38482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 230892
85.7%
Latin 38482
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55071
23.9%
1 54433
23.6%
2 45783
19.8%
- 38482
16.7%
9 10210
 
4.4%
3 9558
 
4.1%
4 6772
 
2.9%
8 3530
 
1.5%
7 2966
 
1.3%
6 2303
 
1.0%
Latin
ValueCountFrequency (%)
Q 38482
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55071
20.4%
1 54433
20.2%
2 45783
17.0%
- 38482
14.3%
Q 38482
14.3%
9 10210
 
3.8%
3 9558
 
3.5%
4 6772
 
2.5%
8 3530
 
1.3%
7 2966
 
1.1%
Other values (2) 4087
 
1.5%

founded_year
Real number (ℝ)

Distinct103
Distinct (%)0.3%
Missing15812
Missing (%)29.1%
Infinite0
Infinite (%)0.0%
Mean2007.3591
Minimum1902
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:12.334658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1902
5-th percentile1996
Q12006
median2010
Q32012
95-th percentile2013
Maximum2014
Range112
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.5792031
Coefficient of variation (CV)0.0037757086
Kurtosis39.926643
Mean2007.3591
Median Absolute Deviation (MAD)3
Skewness-4.6668042
Sum77247194
Variance57.444319
MonotonicityNot monotonic
2023-07-04T09:30:12.486620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2012 5211
 
9.6%
2011 4905
 
9.0%
2013 4044
 
7.4%
2010 3773
 
6.9%
2009 2979
 
5.5%
2008 2348
 
4.3%
2007 2331
 
4.3%
2006 1807
 
3.3%
2014 1469
 
2.7%
2005 1418
 
2.6%
Other values (93) 8197
15.1%
(Missing) 15812
29.1%
ValueCountFrequency (%)
1902 2
 
< 0.1%
1903 1
 
< 0.1%
1905 1
 
< 0.1%
1906 5
< 0.1%
1907 1
 
< 0.1%
1908 1
 
< 0.1%
1910 2
 
< 0.1%
1911 2
 
< 0.1%
1912 6
< 0.1%
1913 2
 
< 0.1%
ValueCountFrequency (%)
2014 1469
 
2.7%
2013 4044
7.4%
2012 5211
9.6%
2011 4905
9.0%
2010 3773
6.9%
2009 2979
5.5%
2008 2348
4.3%
2007 2331
4.3%
2006 1807
 
3.3%
2005 1418
 
2.6%

first_funding_at
Categorical

HIGH CARDINALITY  MISSING 

Distinct3914
Distinct (%)7.9%
Missing4856
Missing (%)8.9%
Memory size424.3 KiB
2012-01-01
 
468
2013-01-01
 
463
2008-01-01
 
422
2011-01-01
 
392
2007-01-01
 
342
Other values (3909)
47351 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters494380
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique781 ?
Unique (%)1.6%

Sample

1st row2012-06-30
2nd row2010-06-04
3rd row2012-08-09
4th row2011-04-01
5th row2014-08-17

Common Values

ValueCountFrequency (%)
2012-01-01 468
 
0.9%
2013-01-01 463
 
0.9%
2008-01-01 422
 
0.8%
2011-01-01 392
 
0.7%
2007-01-01 342
 
0.6%
2014-01-01 336
 
0.6%
2010-01-01 324
 
0.6%
2009-01-01 290
 
0.5%
2013-09-01 221
 
0.4%
2006-01-01 217
 
0.4%
Other values (3904) 45963
84.7%
(Missing) 4856
 
8.9%

Length

2023-07-04T09:30:12.669794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-01-01 468
 
0.9%
2013-01-01 463
 
0.9%
2008-01-01 422
 
0.9%
2011-01-01 392
 
0.8%
2007-01-01 342
 
0.7%
2014-01-01 336
 
0.7%
2010-01-01 324
 
0.7%
2009-01-01 290
 
0.6%
2013-09-01 221
 
0.4%
2006-01-01 217
 
0.4%
Other values (3904) 45963
93.0%

Most occurring characters

ValueCountFrequency (%)
0 137882
27.9%
- 98876
20.0%
1 93806
19.0%
2 77448
15.7%
3 18881
 
3.8%
4 15912
 
3.2%
9 12450
 
2.5%
8 10403
 
2.1%
7 9743
 
2.0%
6 9711
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 395504
80.0%
Dash Punctuation 98876
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 137882
34.9%
1 93806
23.7%
2 77448
19.6%
3 18881
 
4.8%
4 15912
 
4.0%
9 12450
 
3.1%
8 10403
 
2.6%
7 9743
 
2.5%
6 9711
 
2.5%
5 9268
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 98876
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494380
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 137882
27.9%
- 98876
20.0%
1 93806
19.0%
2 77448
15.7%
3 18881
 
3.8%
4 15912
 
3.2%
9 12450
 
2.5%
8 10403
 
2.1%
7 9743
 
2.0%
6 9711
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 137882
27.9%
- 98876
20.0%
1 93806
19.0%
2 77448
15.7%
3 18881
 
3.8%
4 15912
 
3.2%
9 12450
 
2.5%
8 10403
 
2.1%
7 9743
 
2.0%
6 9711
 
2.0%

last_funding_at
Categorical

HIGH CARDINALITY  MISSING 

Distinct3657
Distinct (%)7.4%
Missing4856
Missing (%)8.9%
Memory size424.3 KiB
2013-01-01
 
387
2014-01-01
 
364
2012-01-01
 
348
2008-01-01
 
302
2011-01-01
 
272
Other values (3652)
47765 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters494380
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique771 ?
Unique (%)1.6%

Sample

1st row2012-06-30
2nd row2010-09-23
3rd row2012-08-09
4th row2011-04-01
5th row2014-09-26

Common Values

ValueCountFrequency (%)
2013-01-01 387
 
0.7%
2014-01-01 364
 
0.7%
2012-01-01 348
 
0.6%
2008-01-01 302
 
0.6%
2011-01-01 272
 
0.5%
2013-09-01 241
 
0.4%
2010-01-01 210
 
0.4%
2014-06-01 209
 
0.4%
2007-01-01 202
 
0.4%
2009-01-01 200
 
0.4%
Other values (3647) 46703
86.0%
(Missing) 4856
 
8.9%

Length

2023-07-04T09:30:12.865037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-01-01 387
 
0.8%
2014-01-01 364
 
0.7%
2012-01-01 348
 
0.7%
2008-01-01 302
 
0.6%
2011-01-01 272
 
0.6%
2013-09-01 241
 
0.5%
2010-01-01 210
 
0.4%
2014-06-01 209
 
0.4%
2007-01-01 202
 
0.4%
2009-01-01 200
 
0.4%
Other values (3647) 46703
94.5%

Most occurring characters

ValueCountFrequency (%)
0 130682
26.4%
- 98876
20.0%
1 95314
19.3%
2 77981
15.8%
4 21742
 
4.4%
3 20526
 
4.2%
9 11503
 
2.3%
8 10267
 
2.1%
7 9579
 
1.9%
6 9180
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 395504
80.0%
Dash Punctuation 98876
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130682
33.0%
1 95314
24.1%
2 77981
19.7%
4 21742
 
5.5%
3 20526
 
5.2%
9 11503
 
2.9%
8 10267
 
2.6%
7 9579
 
2.4%
6 9180
 
2.3%
5 8730
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 98876
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494380
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130682
26.4%
- 98876
20.0%
1 95314
19.3%
2 77981
15.8%
4 21742
 
4.4%
3 20526
 
4.2%
9 11503
 
2.3%
8 10267
 
2.1%
7 9579
 
1.9%
6 9180
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130682
26.4%
- 98876
20.0%
1 95314
19.3%
2 77981
15.8%
4 21742
 
4.4%
3 20526
 
4.2%
9 11503
 
2.3%
8 10267
 
2.1%
7 9579
 
1.9%
6 9180
 
1.9%

seed
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3337
Distinct (%)6.7%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean217321.5
Minimum0
Maximum1.3 × 108
Zeros35598
Zeros (%)65.6%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:13.032568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325000
95-th percentile1400000
Maximum1.3 × 108
Range1.3 × 108
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation1056984.8
Coefficient of variation (CV)4.863692
Kurtosis6506.6142
Mean217321.5
Median Absolute Deviation (MAD)0
Skewness61.541571
Sum1.074394 × 1010
Variance1.1172169 × 1012
MonotonicityNot monotonic
2023-07-04T09:30:13.204106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35598
65.6%
40000 654
 
1.2%
1000000 570
 
1.0%
500000 543
 
1.0%
100000 538
 
1.0%
50000 406
 
0.7%
250000 341
 
0.6%
25000 322
 
0.6%
2000000 316
 
0.6%
20000 315
 
0.6%
Other values (3327) 9835
 
18.1%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 35598
65.6%
14 1
 
< 0.1%
60 1
 
< 0.1%
100 1
 
< 0.1%
118 1
 
< 0.1%
150 1
 
< 0.1%
890 1
 
< 0.1%
929 1
 
< 0.1%
1000 27
 
< 0.1%
1333 1
 
< 0.1%
ValueCountFrequency (%)
130000000 1
< 0.1%
100000000 1
< 0.1%
64000000 1
< 0.1%
25000000 2
< 0.1%
24833177 1
< 0.1%
22300000 1
< 0.1%
21000000 2
< 0.1%
15027818 1
< 0.1%
15000000 1
< 0.1%
14081347 1
< 0.1%

venture
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct9300
Distinct (%)18.8%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean7501050.5
Minimum0
Maximum2.351 × 109
Zeros26161
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:13.392586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35000000
95-th percentile38000000
Maximum2.351 × 109
Range2.351 × 109
Interquartile range (IQR)5000000

Descriptive statistics

Standard deviation28471124
Coefficient of variation (CV)3.7956182
Kurtosis1338.3873
Mean7501050.5
Median Absolute Deviation (MAD)0
Skewness24.675992
Sum3.7083694 × 1011
Variance8.1060491 × 1014
MonotonicityNot monotonic
2023-07-04T09:30:13.604358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26161
48.2%
10000000 493
 
0.9%
5000000 487
 
0.9%
1000000 470
 
0.9%
2000000 422
 
0.8%
3000000 371
 
0.7%
4000000 318
 
0.6%
6000000 297
 
0.5%
1500000 251
 
0.5%
15000000 241
 
0.4%
Other values (9290) 19927
36.7%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 26161
48.2%
22 1
 
< 0.1%
291 1
 
< 0.1%
715 1
 
< 0.1%
1000 2
 
< 0.1%
1100 1
 
< 0.1%
1265 1
 
< 0.1%
1305 1
 
< 0.1%
1355 1
 
< 0.1%
1500 2
 
< 0.1%
ValueCountFrequency (%)
2351000000 1
< 0.1%
1506000000 1
< 0.1%
1201000000 1
< 0.1%
1136000000 1
< 0.1%
866550786 1
< 0.1%
818225039 1
< 0.1%
794200000 1
< 0.1%
775000000 1
< 0.1%
762000000 1
< 0.1%
760166511 1
< 0.1%

equity_crowdfunding
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct252
Distinct (%)0.5%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean6163.3221
Minimum0
Maximum25000000
Zeros48916
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:13.941613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum25000000
Range25000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation199904.82
Coefficient of variation (CV)32.434588
Kurtosis7198.2505
Mean6163.3221
Median Absolute Deviation (MAD)0
Skewness73.801297
Sum3.0470232 × 108
Variance3.9961935 × 1010
MonotonicityNot monotonic
2023-07-04T09:30:14.134335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48916
90.1%
100000 22
 
< 0.1%
50000 19
 
< 0.1%
1000 16
 
< 0.1%
500000 14
 
< 0.1%
10000 14
 
< 0.1%
250000 12
 
< 0.1%
30000 12
 
< 0.1%
5000 12
 
< 0.1%
20000 12
 
< 0.1%
Other values (242) 389
 
0.7%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 48916
90.1%
50 1
 
< 0.1%
80 1
 
< 0.1%
100 5
 
< 0.1%
102 1
 
< 0.1%
144 1
 
< 0.1%
300 1
 
< 0.1%
317 1
 
< 0.1%
400 1
 
< 0.1%
500 2
 
< 0.1%
ValueCountFrequency (%)
25000000 1
< 0.1%
17000000 1
< 0.1%
15808674 1
< 0.1%
10020000 1
< 0.1%
8481852 1
< 0.1%
7200000 1
< 0.1%
7000000 1
< 0.1%
6889180 1
< 0.1%
6000000 2
< 0.1%
5918360 1
< 0.1%

undisclosed
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct687
Distinct (%)1.4%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean130221.28
Minimum0
Maximum2.9243283 × 108
Zeros48486
Zeros (%)89.3%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:14.340861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.9243283 × 108
Range2.9243283 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2981403.7
Coefficient of variation (CV)22.894903
Kurtosis4473.7635
Mean130221.28
Median Absolute Deviation (MAD)0
Skewness57.589581
Sum6.4378797 × 109
Variance8.8887677 × 1012
MonotonicityNot monotonic
2023-07-04T09:30:14.492663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48486
89.3%
500000 20
 
< 0.1%
100000 18
 
< 0.1%
1000000 18
 
< 0.1%
2000000 16
 
< 0.1%
200000 14
 
< 0.1%
270862 14
 
< 0.1%
300000 13
 
< 0.1%
250000 12
 
< 0.1%
10000000 11
 
< 0.1%
Other values (677) 816
 
1.5%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 48486
89.3%
1 1
 
< 0.1%
344 1
 
< 0.1%
1290 1
 
< 0.1%
2000 1
 
< 0.1%
2500 1
 
< 0.1%
4000 1
 
< 0.1%
4500 1
 
< 0.1%
5000 1
 
< 0.1%
8950 1
 
< 0.1%
ValueCountFrequency (%)
292432833 1
< 0.1%
259417808 1
< 0.1%
250800000 1
< 0.1%
147000000 1
< 0.1%
125000000 1
< 0.1%
116983286 1
< 0.1%
103300000 1
< 0.1%
101000000 1
< 0.1%
100000000 2
< 0.1%
91000000 1
< 0.1%

convertible_note
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct299
Distinct (%)0.6%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean23364.098
Minimum0
Maximum3 × 108
Zeros48881
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:14.656584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3 × 108
Range3 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1432045.7
Coefficient of variation (CV)61.292575
Kurtosis39047.597
Mean23364.098
Median Absolute Deviation (MAD)0
Skewness188.85866
Sum1.1550743 × 109
Variance2.050755 × 1012
MonotonicityNot monotonic
2023-07-04T09:30:14.834438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48881
90.0%
100000 20
 
< 0.1%
500000 20
 
< 0.1%
1000000 15
 
< 0.1%
50000 15
 
< 0.1%
250000 14
 
< 0.1%
1500000 13
 
< 0.1%
150000 13
 
< 0.1%
200000 11
 
< 0.1%
2000000 9
 
< 0.1%
Other values (289) 427
 
0.8%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 48881
90.0%
30 1
 
< 0.1%
100 1
 
< 0.1%
500 2
 
< 0.1%
1000 2
 
< 0.1%
1750 1
 
< 0.1%
2000 2
 
< 0.1%
5000 3
 
< 0.1%
5200 1
 
< 0.1%
6000 2
 
< 0.1%
ValueCountFrequency (%)
300000000 1
< 0.1%
60000000 1
< 0.1%
46000000 1
< 0.1%
38979412 1
< 0.1%
25000000 1
< 0.1%
20000000 1
< 0.1%
18472424 1
< 0.1%
16251091 1
< 0.1%
14025045 1
< 0.1%
13738976 1
< 0.1%

debt_financing
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1872
Distinct (%)3.8%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean1888156.9
Minimum0
Maximum3.0079503 × 1010
Zeros45213
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:15.042763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile970596.8
Maximum3.0079503 × 1010
Range3.0079503 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3820457 × 108
Coefficient of variation (CV)73.195489
Kurtosis45388.908
Mean1888156.9
Median Absolute Deviation (MAD)0
Skewness209.06015
Sum9.33467 × 1010
Variance1.9100502 × 1016
MonotonicityNot monotonic
2023-07-04T09:30:15.241426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45213
83.3%
1000000 117
 
0.2%
500000 99
 
0.2%
100000 92
 
0.2%
2000000 86
 
0.2%
3000000 85
 
0.2%
5000000 76
 
0.1%
200000 71
 
0.1%
1500000 60
 
0.1%
250000 58
 
0.1%
Other values (1862) 3481
 
6.4%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 45213
83.3%
100 1
 
< 0.1%
400 1
 
< 0.1%
500 1
 
< 0.1%
1200 1
 
< 0.1%
1500 1
 
< 0.1%
2000 1
 
< 0.1%
2500 3
 
< 0.1%
3750 1
 
< 0.1%
4000 1
 
< 0.1%
ValueCountFrequency (%)
3.0079503 × 10101
 
< 0.1%
3200000000 1
 
< 0.1%
2400000000 1
 
< 0.1%
2250000000 1
 
< 0.1%
1500000000 1
 
< 0.1%
1200000000 2
< 0.1%
940000000 1
 
< 0.1%
770000000 1
 
< 0.1%
750000000 3
< 0.1%
743000000 1
 
< 0.1%

angel
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct999
Distinct (%)2.0%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean65418.98
Minimum0
Maximum63590263
Zeros46309
Zeros (%)85.3%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:15.422205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile162954
Maximum63590263
Range63590263
Interquartile range (IQR)0

Descriptive statistics

Standard deviation658290.79
Coefficient of variation (CV)10.062688
Kurtosis2860.5858
Mean65418.98
Median Absolute Deviation (MAD)0
Skewness42.16929
Sum3.2341835 × 109
Variance4.3334676 × 1011
MonotonicityNot monotonic
2023-07-04T09:30:15.579779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46309
85.3%
500000 195
 
0.4%
1000000 189
 
0.3%
100000 123
 
0.2%
250000 119
 
0.2%
300000 91
 
0.2%
200000 89
 
0.2%
1500000 85
 
0.2%
2000000 81
 
0.1%
400000 70
 
0.1%
Other values (989) 2087
 
3.8%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 46309
85.3%
23 1
 
< 0.1%
120 1
 
< 0.1%
123 1
 
< 0.1%
439 1
 
< 0.1%
1000 1
 
< 0.1%
1200 1
 
< 0.1%
1350 1
 
< 0.1%
1500 1
 
< 0.1%
1635 1
 
< 0.1%
ValueCountFrequency (%)
63590263 1
 
< 0.1%
43923865 1
 
< 0.1%
40000000 1
 
< 0.1%
30254390 1
 
< 0.1%
30000000 2
 
< 0.1%
20708316 1
 
< 0.1%
20000000 5
< 0.1%
18000000 1
 
< 0.1%
16837481 1
 
< 0.1%
15424164 1
 
< 0.1%

grant
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct532
Distinct (%)1.1%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean162845.28
Minimum0
Maximum7.505 × 108
Zeros48296
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:15.786399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7.505 × 108
Range7.505 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5612088
Coefficient of variation (CV)34.462701
Kurtosis8858.0422
Mean162845.28
Median Absolute Deviation (MAD)0
Skewness83.318249
Sum8.0507448 × 109
Variance3.1495532 × 1013
MonotonicityNot monotonic
2023-07-04T09:30:15.994457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48296
89.0%
25000 37
 
0.1%
50000 36
 
0.1%
40000 33
 
0.1%
100000 33
 
0.1%
500000 27
 
< 0.1%
1000000 27
 
< 0.1%
150000 26
 
< 0.1%
2000000 23
 
< 0.1%
3000000 23
 
< 0.1%
Other values (522) 877
 
1.6%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 48296
89.0%
300 1
 
< 0.1%
1003 1
 
< 0.1%
1700 1
 
< 0.1%
1840 1
 
< 0.1%
2000 2
 
< 0.1%
2200 1
 
< 0.1%
2393 1
 
< 0.1%
2500 1
 
< 0.1%
3000 4
 
< 0.1%
ValueCountFrequency (%)
750500000 1
< 0.1%
477475356 1
< 0.1%
412000000 1
< 0.1%
400000000 1
< 0.1%
251860000 1
< 0.1%
206000000 1
< 0.1%
191000000 1
< 0.1%
180000000 1
< 0.1%
170000000 1
< 0.1%
154053900 1
< 0.1%

private_equity
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct847
Distinct (%)1.7%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean2074285.7
Minimum0
Maximum3.5 × 109
Zeros48065
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:16.248910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3.5 × 109
Range3.5 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation31672313
Coefficient of variation (CV)15.269021
Kurtosis4357.7056
Mean2074285.7
Median Absolute Deviation (MAD)0
Skewness51.557994
Sum1.0254854 × 1011
Variance1.0031354 × 1015
MonotonicityNot monotonic
2023-07-04T09:30:16.416460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48065
88.5%
100000000 39
 
0.1%
5000000 30
 
0.1%
10000000 22
 
< 0.1%
20000000 18
 
< 0.1%
25000000 15
 
< 0.1%
2000000 15
 
< 0.1%
60000000 15
 
< 0.1%
50000000 14
 
< 0.1%
200000000 13
 
< 0.1%
Other values (837) 1192
 
2.2%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 48065
88.5%
60 1
 
< 0.1%
1660 1
 
< 0.1%
2000 1
 
< 0.1%
3064 1
 
< 0.1%
4000 1
 
< 0.1%
6000 1
 
< 0.1%
8000 1
 
< 0.1%
10000 3
 
< 0.1%
13000 1
 
< 0.1%
ValueCountFrequency (%)
3500000000 1
< 0.1%
2600000000 1
< 0.1%
1710000000 1
< 0.1%
1498515340 1
< 0.1%
1107000000 1
< 0.1%
1050000000 1
< 0.1%
980000000 1
< 0.1%
771000000 1
< 0.1%
750000000 1
< 0.1%
727000000 1
< 0.1%

post_ipo_equity
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct239
Distinct (%)0.5%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean608873.65
Minimum0
Maximum4.7 × 109
Zeros49122
Zeros (%)90.5%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:16.643025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.7 × 109
Range4.7 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26783481
Coefficient of variation (CV)43.988569
Kurtosis19769.842
Mean608873.65
Median Absolute Deviation (MAD)0
Skewness122.82621
Sum3.0101495 × 1010
Variance7.1735483 × 1014
MonotonicityNot monotonic
2023-07-04T09:30:16.809539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49122
90.5%
20000000 8
 
< 0.1%
5000000 7
 
< 0.1%
10000000 7
 
< 0.1%
12000000 6
 
< 0.1%
6000000 4
 
< 0.1%
40000000 4
 
< 0.1%
100000000 4
 
< 0.1%
30000000 4
 
< 0.1%
1300000 3
 
< 0.1%
Other values (229) 269
 
0.5%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 49122
90.5%
10500 1
 
< 0.1%
119238 1
 
< 0.1%
146071 1
 
< 0.1%
150000 1
 
< 0.1%
155519 1
 
< 0.1%
216000 1
 
< 0.1%
300000 2
 
< 0.1%
353500 1
 
< 0.1%
375000 1
 
< 0.1%
ValueCountFrequency (%)
4700000000 1
< 0.1%
1662513431 1
< 0.1%
1200000000 1
< 0.1%
1100000000 1
< 0.1%
1000000000 2
< 0.1%
804000000 1
< 0.1%
750000000 1
< 0.1%
739265776 1
< 0.1%
736000000 1
< 0.1%
700000000 1
< 0.1%

post_ipo_debt
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct57
Distinct (%)0.1%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean443435.97
Minimum0
Maximum5.8 × 109
Zeros49363
Zeros (%)90.9%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:17.032178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5.8 × 109
Range5.8 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34281690
Coefficient of variation (CV)77.309221
Kurtosis19232.073
Mean443435.97
Median Absolute Deviation (MAD)0
Skewness128.65385
Sum2.1922588 × 1010
Variance1.1752342 × 1015
MonotonicityNot monotonic
2023-07-04T09:30:17.467597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49363
90.9%
5000000 4
 
< 0.1%
10000000 4
 
< 0.1%
35000000 3
 
< 0.1%
25000000 3
 
< 0.1%
20000000 3
 
< 0.1%
150000000 3
 
< 0.1%
30000000 3
 
< 0.1%
50000000 2
 
< 0.1%
2000000 2
 
< 0.1%
Other values (47) 48
 
0.1%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 49363
90.9%
90750 1
 
< 0.1%
300000 1
 
< 0.1%
430000 1
 
< 0.1%
590000 1
 
< 0.1%
800000 1
 
< 0.1%
1100000 1
 
< 0.1%
1500000 1
 
< 0.1%
2000000 2
 
< 0.1%
3500000 1
 
< 0.1%
ValueCountFrequency (%)
5800000000 1
< 0.1%
3500000000 1
< 0.1%
2000000000 1
< 0.1%
1900000000 1
< 0.1%
1055750000 1
< 0.1%
920000000 1
< 0.1%
800000000 1
< 0.1%
725000000 1
< 0.1%
600000000 1
< 0.1%
500000000 2
< 0.1%

secondary_market
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean38455.922
Minimum0
Maximum6.8061155 × 108
Zeros49419
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:17.666933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.8061155 × 108
Range6.8061155 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3864460.6
Coefficient of variation (CV)100.49065
Kurtosis22129.124
Mean38455.922
Median Absolute Deviation (MAD)0
Skewness140.01897
Sum1.9011839 × 109
Variance1.4934056 × 1013
MonotonicityNot monotonic
2023-07-04T09:30:17.820477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 49419
91.0%
210000000 1
 
< 0.1%
19300000 1
 
< 0.1%
7718867 1
 
< 0.1%
680611554 1
 
< 0.1%
400000000 1
 
< 0.1%
20000000 1
 
< 0.1%
700000 1
 
< 0.1%
210000 1
 
< 0.1%
2500000 1
 
< 0.1%
Other values (10) 10
 
< 0.1%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 49419
91.0%
156929 1
 
< 0.1%
210000 1
 
< 0.1%
700000 1
 
< 0.1%
2500000 1
 
< 0.1%
4700000 1
 
< 0.1%
6192396 1
 
< 0.1%
7343789 1
 
< 0.1%
7718867 1
 
< 0.1%
12500000 1
 
< 0.1%
ValueCountFrequency (%)
680611554 1
< 0.1%
400000000 1
< 0.1%
210000000 1
< 0.1%
200000000 1
< 0.1%
126700000 1
< 0.1%
78800355 1
< 0.1%
63750000 1
< 0.1%
60000000 1
< 0.1%
20000000 1
< 0.1%
19300000 1
< 0.1%

product_crowdfunding
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct176
Distinct (%)0.4%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean7074.2271
Minimum0
Maximum72000000
Zeros49225
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:18.078991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72000000
Range72000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation428216.59
Coefficient of variation (CV)60.531926
Kurtosis20631.918
Mean7074.2271
Median Absolute Deviation (MAD)0
Skewness135.19128
Sum3.4973564 × 108
Variance1.8336945 × 1011
MonotonicityNot monotonic
2023-07-04T09:30:18.351496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49225
90.7%
1000000 7
 
< 0.1%
500000 6
 
< 0.1%
100000 5
 
< 0.1%
600000 4
 
< 0.1%
10000 4
 
< 0.1%
300000 3
 
< 0.1%
4545754 2
 
< 0.1%
1400000 2
 
< 0.1%
1515251 2
 
< 0.1%
Other values (166) 178
 
0.3%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 49225
90.7%
215 1
 
< 0.1%
428 1
 
< 0.1%
1000 1
 
< 0.1%
1505 1
 
< 0.1%
2752 1
 
< 0.1%
5116 1
 
< 0.1%
10000 4
 
< 0.1%
10645 1
 
< 0.1%
11567 1
 
< 0.1%
ValueCountFrequency (%)
72000000 1
< 0.1%
52000000 1
< 0.1%
14600000 1
< 0.1%
13300000 1
< 0.1%
11008376 1
< 0.1%
10300000 1
< 0.1%
10000000 1
< 0.1%
8600000 1
< 0.1%
7216365 1
< 0.1%
6225354 1
< 0.1%

round_A
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2035
Distinct (%)4.1%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean1243955
Minimum0
Maximum3.19 × 108
Zeros40435
Zeros (%)74.5%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:18.556871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7000000
Maximum3.19 × 108
Range3.19 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5531974
Coefficient of variation (CV)4.4470853
Kurtosis752.81382
Mean1243955
Median Absolute Deviation (MAD)0
Skewness19.776802
Sum6.1498648 × 1010
Variance3.0602737 × 1013
MonotonicityNot monotonic
2023-07-04T09:30:18.785365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40435
74.5%
5000000 525
 
1.0%
3000000 378
 
0.7%
2000000 372
 
0.7%
10000000 324
 
0.6%
1000000 323
 
0.6%
4000000 322
 
0.6%
6000000 308
 
0.6%
1500000 219
 
0.4%
7000000 196
 
0.4%
Other values (2025) 6036
 
11.1%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 40435
74.5%
4 1
 
< 0.1%
291 1
 
< 0.1%
715 1
 
< 0.1%
1700 1
 
< 0.1%
2000 1
 
< 0.1%
6500 1
 
< 0.1%
7000 2
 
< 0.1%
10000 1
 
< 0.1%
12989 1
 
< 0.1%
ValueCountFrequency (%)
319000000 1
< 0.1%
300000000 1
< 0.1%
260000000 1
< 0.1%
225000000 2
< 0.1%
200000000 1
< 0.1%
176000000 1
< 0.1%
170604000 1
< 0.1%
165000000 1
< 0.1%
150000000 1
< 0.1%
130000000 1
< 0.1%

round_B
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1269
Distinct (%)2.6%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean1492891.2
Minimum0
Maximum5.42 × 108
Zeros43991
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:18.979071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10000000
Maximum5.42 × 108
Range5.42 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7472704.5
Coefficient of variation (CV)5.0055253
Kurtosis927.98287
Mean1492891.2
Median Absolute Deviation (MAD)0
Skewness20.445337
Sum7.3805553 × 1010
Variance5.5841312 × 1013
MonotonicityNot monotonic
2023-07-04T09:30:19.260619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43991
81.0%
10000000 391
 
0.7%
15000000 206
 
0.4%
8000000 201
 
0.4%
5000000 184
 
0.3%
20000000 173
 
0.3%
12000000 163
 
0.3%
6000000 159
 
0.3%
7000000 139
 
0.3%
4000000 113
 
0.2%
Other values (1259) 3718
 
6.8%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 43991
81.0%
1000 1
 
< 0.1%
2000 1
 
< 0.1%
2666 1
 
< 0.1%
4000 1
 
< 0.1%
29983 1
 
< 0.1%
30488 1
 
< 0.1%
50000 4
 
< 0.1%
88888 1
 
< 0.1%
100000 3
 
< 0.1%
ValueCountFrequency (%)
542000000 1
< 0.1%
355187000 1
< 0.1%
350000000 1
< 0.1%
320000000 1
< 0.1%
250000000 1
< 0.1%
200000000 1
< 0.1%
185000000 1
< 0.1%
165000000 1
< 0.1%
160000000 1
< 0.1%
157000000 1
< 0.1%

round_C
Real number (ℝ)

MISSING  ZEROS 

Distinct740
Distinct (%)1.5%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean1205355.8
Minimum0
Maximum4.9 × 108
Zeros46601
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:19.670166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5000000
Maximum4.9 × 108
Range4.9 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7993591.7
Coefficient of variation (CV)6.6317279
Kurtosis691.38682
Mean1205355.8
Median Absolute Deviation (MAD)0
Skewness19.007129
Sum5.959038 × 1010
Variance6.3897509 × 1013
MonotonicityNot monotonic
2023-07-04T09:30:19.999334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46601
85.8%
10000000 164
 
0.3%
15000000 159
 
0.3%
20000000 129
 
0.2%
25000000 97
 
0.2%
12000000 89
 
0.2%
30000000 79
 
0.1%
5000000 63
 
0.1%
8000000 58
 
0.1%
6000000 55
 
0.1%
Other values (730) 1944
 
3.6%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 46601
85.8%
2000 1
 
< 0.1%
76265 1
 
< 0.1%
100000 1
 
< 0.1%
150000 1
 
< 0.1%
160000 1
 
< 0.1%
187539 1
 
< 0.1%
199999 1
 
< 0.1%
200000 1
 
< 0.1%
225417 1
 
< 0.1%
ValueCountFrequency (%)
490000000 1
 
< 0.1%
375000000 1
 
< 0.1%
350000000 2
< 0.1%
300000000 1
 
< 0.1%
258000000 1
 
< 0.1%
216000000 1
 
< 0.1%
200000000 3
< 0.1%
198730677 1
 
< 0.1%
194000000 1
 
< 0.1%
160000000 2
< 0.1%

round_D
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct458
Distinct (%)0.9%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean737526.06
Minimum0
Maximum1.2 × 109
Zeros48150
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:20.298906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.2 × 109
Range1.2 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9815218.2
Coefficient of variation (CV)13.3083
Kurtosis6550.4541
Mean737526.06
Median Absolute Deviation (MAD)0
Skewness64.292553
Sum3.6461813 × 1010
Variance9.6338508 × 1013
MonotonicityNot monotonic
2023-07-04T09:30:20.626513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48150
88.7%
10000000 67
 
0.1%
20000000 64
 
0.1%
15000000 59
 
0.1%
12000000 44
 
0.1%
25000000 42
 
0.1%
50000000 33
 
0.1%
30000000 33
 
0.1%
40000000 26
 
< 0.1%
8000000 26
 
< 0.1%
Other values (448) 894
 
1.6%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 48150
88.7%
5000 1
 
< 0.1%
85000 1
 
< 0.1%
100000 1
 
< 0.1%
122216 1
 
< 0.1%
124233 1
 
< 0.1%
150000 1
 
< 0.1%
152037 1
 
< 0.1%
200000 1
 
< 0.1%
269228 1
 
< 0.1%
ValueCountFrequency (%)
1200000000 1
 
< 0.1%
950000000 1
 
< 0.1%
475000000 1
 
< 0.1%
420000000 1
 
< 0.1%
300000000 1
 
< 0.1%
280000000 1
 
< 0.1%
250000000 2
< 0.1%
220000000 1
 
< 0.1%
210000000 1
 
< 0.1%
200000000 4
< 0.1%

round_E
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct225
Distinct (%)0.5%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean342468.2
Minimum0
Maximum4 × 108
Zeros48922
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:20.978019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4 × 108
Range4 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5406914.6
Coefficient of variation (CV)15.788078
Kurtosis1595.4219
Mean342468.2
Median Absolute Deviation (MAD)0
Skewness32.900867
Sum1.6930943 × 1010
Variance2.9234725 × 1013
MonotonicityNot monotonic
2023-07-04T09:30:21.270651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48922
90.1%
20000000 24
 
< 0.1%
25000000 20
 
< 0.1%
10000000 17
 
< 0.1%
30000000 17
 
< 0.1%
40000000 16
 
< 0.1%
15000000 15
 
< 0.1%
50000000 14
 
< 0.1%
12000000 13
 
< 0.1%
8000000 13
 
< 0.1%
Other values (215) 367
 
0.7%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 48922
90.1%
327054 1
 
< 0.1%
487500 1
 
< 0.1%
539999 1
 
< 0.1%
980000 1
 
< 0.1%
1200000 1
 
< 0.1%
1500750 1
 
< 0.1%
1800000 1
 
< 0.1%
1867973 1
 
< 0.1%
2000000 3
 
< 0.1%
ValueCountFrequency (%)
400000000 1
< 0.1%
360000000 1
< 0.1%
250000000 2
< 0.1%
227000000 1
< 0.1%
225000000 1
< 0.1%
220000000 1
< 0.1%
200000000 1
< 0.1%
175445382 1
< 0.1%
170000000 1
< 0.1%
167700000 1
< 0.1%

round_F
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct110
Distinct (%)0.2%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean169769.19
Minimum0
Maximum1.06 × 109
Zeros49266
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:21.614047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.06 × 109
Range1.06 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6277905.5
Coefficient of variation (CV)36.979062
Kurtosis16828.06
Mean169769.19
Median Absolute Deviation (MAD)0
Skewness109.22726
Sum8.3930492 × 109
Variance3.9412097 × 1013
MonotonicityNot monotonic
2023-07-04T09:30:21.836395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49266
90.7%
20000000 12
 
< 0.1%
50000000 9
 
< 0.1%
40000000 7
 
< 0.1%
25000000 6
 
< 0.1%
15000000 5
 
< 0.1%
30000000 4
 
< 0.1%
35000000 4
 
< 0.1%
7000000 4
 
< 0.1%
10000000 4
 
< 0.1%
Other values (100) 117
 
0.2%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 49266
90.7%
60000 1
 
< 0.1%
271500 1
 
< 0.1%
658362 1
 
< 0.1%
800000 1
 
< 0.1%
1000000 1
 
< 0.1%
1160000 1
 
< 0.1%
1999999 1
 
< 0.1%
2000000 1
 
< 0.1%
2425101 1
 
< 0.1%
ValueCountFrequency (%)
1060000000 1
 
< 0.1%
286000000 1
 
< 0.1%
250000000 1
 
< 0.1%
249101159 1
 
< 0.1%
230000000 1
 
< 0.1%
225000000 1
 
< 0.1%
210000000 1
 
< 0.1%
200000000 3
< 0.1%
176000000 1
 
< 0.1%
150000000 2
< 0.1%

round_G
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct32
Distinct (%)0.1%
Missing4856
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean57670.668
Minimum0
Maximum1 × 109
Zeros49404
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size424.3 KiB
2023-07-04T09:30:22.209337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1 × 109
Range1 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5252311.9
Coefficient of variation (CV)91.074235
Kurtosis27683.541
Mean57670.668
Median Absolute Deviation (MAD)0
Skewness155.72713
Sum2.8511225 × 109
Variance2.7586781 × 1013
MonotonicityNot monotonic
2023-07-04T09:30:22.343991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 49404
91.0%
10000000 3
 
< 0.1%
25000000 2
 
< 0.1%
16084571 1
 
< 0.1%
172000000 1
 
< 0.1%
63000000 1
 
< 0.1%
100000000 1
 
< 0.1%
400000000 1
 
< 0.1%
73667711 1
 
< 0.1%
99000000 1
 
< 0.1%
Other values (22) 22
 
< 0.1%
(Missing) 4856
 
8.9%
ValueCountFrequency (%)
0 49404
91.0%
818427 1
 
< 0.1%
1700000 1
 
< 0.1%
3638297 1
 
< 0.1%
3700000 1
 
< 0.1%
6800000 1
 
< 0.1%
6937919 1
 
< 0.1%
8199999 1
 
< 0.1%
10000000 3
 
< 0.1%
12500000 1
 
< 0.1%
ValueCountFrequency (%)
1000000000 1
< 0.1%
400000000 1
< 0.1%
350000000 1
< 0.1%
172000000 1
< 0.1%
100000000 1
< 0.1%
99000000 1
< 0.1%
73667711 1
< 0.1%
69985435 1
< 0.1%
63000000 1
< 0.1%
60000000 1
< 0.1%

round_H
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing4856
Missing (%)8.9%
Memory size424.3 KiB
0.0
49434 
50000000.0
 
1
600000000.0
 
1
49000000.0
 
1
4600000.0
 
1

Length

Max length11
Median length3
Mean length3.0005664
Min length3

Characters and Unicode

Total characters148342
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49434
91.0%
50000000.0 1
 
< 0.1%
600000000.0 1
 
< 0.1%
49000000.0 1
 
< 0.1%
4600000.0 1
 
< 0.1%
(Missing) 4856
 
8.9%

Length

2023-07-04T09:30:22.483260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-04T09:30:22.609026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49434
> 99.9%
50000000.0 1
 
< 0.1%
600000000.0 1
 
< 0.1%
49000000.0 1
 
< 0.1%
4600000.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98898
66.7%
. 49438
33.3%
6 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98904
66.7%
Other Punctuation 49438
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98898
> 99.9%
6 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 148342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98898
66.7%
. 49438
33.3%
6 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98898
66.7%
. 49438
33.3%
6 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%

Interactions

2023-07-04T09:29:55.516073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:12.198012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:16.632509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:21.435541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:29.496465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:34.308821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:39.478074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:43.984952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:47.937008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:51.931018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:55.463323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:00.513049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:04.577388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:08.662488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:12.660631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:17.369545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:21.750585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:26.371161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:30.500683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:34.205462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:38.416620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:45.302116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:55.917412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:12.433514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:16.826743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:21.660517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:29.762480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:34.448524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:39.667636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:44.138507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:48.153255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:52.110958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:55.620685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:00.699569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:04.764992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:08.830003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:13.144271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:17.532407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:21.968535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:26.574471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:30.657557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:34.405324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:38.593652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:45.723284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:56.194897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:12.695577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:16.990459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:21.874721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:29.970648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:34.588981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:39.827228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:44.304657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:48.304590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-07-04T09:29:12.260431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:16.985610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:21.312366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:25.930890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:30.138418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:33.873218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:38.047535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:44.404756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:54.619784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:30:00.461563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:16.465473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:21.162158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:29.237549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:34.177599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:39.272619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:43.814335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:47.782550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:51.623665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:28:55.273463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:00.323365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:04.392500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:08.456391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:12.419336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:17.152531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:21.550692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:26.165408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:30.310872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:34.049252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:38.249535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:44.865249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-07-04T09:29:55.042122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-07-04T09:30:22.747165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
funding_roundsfounded_yearseedventureequity_crowdfundingundisclosedconvertible_notedebt_financingangelgrantprivate_equitypost_ipo_equitypost_ipo_debtsecondary_marketproduct_crowdfundinground_Around_Bround_Cround_Dround_Eround_Fround_Gstatusstate_coderound_H
funding_rounds1.000-0.1620.1000.472-0.0450.0430.0820.2750.1140.0610.0860.0420.0190.0190.0070.3510.4180.3430.2460.1600.0980.0430.0610.0310.079
founded_year-0.1621.0000.337-0.4240.1040.0060.042-0.1480.069-0.009-0.107-0.064-0.039-0.0140.045-0.119-0.213-0.209-0.167-0.116-0.068-0.0310.1120.0570.000
seed0.1000.3371.000-0.321-0.048-0.047-0.003-0.1090.003-0.028-0.077-0.043-0.021-0.004-0.014-0.087-0.127-0.108-0.079-0.053-0.032-0.0140.0040.0000.000
venture0.472-0.424-0.3211.000-0.085-0.056-0.0210.094-0.116-0.073-0.001-0.004-0.0100.011-0.0370.5420.5030.3900.2700.1750.1020.0450.0000.0000.505
equity_crowdfunding-0.0450.104-0.048-0.0851.000-0.014-0.005-0.027-0.019-0.012-0.016-0.008-0.004-0.002-0.001-0.044-0.034-0.025-0.017-0.011-0.006-0.0030.0000.0000.000
undisclosed0.0430.006-0.047-0.056-0.0141.000-0.001-0.026-0.017-0.004-0.011-0.002-0.005-0.0030.002-0.032-0.021-0.009-0.010-0.0040.0040.0020.0000.0000.000
convertible_note0.0820.042-0.003-0.021-0.005-0.0011.0000.019-0.0020.008-0.003-0.0010.001-0.002-0.001-0.011-0.006-0.007-0.0050.000-0.0030.0050.0000.0000.000
debt_financing0.275-0.148-0.1090.094-0.027-0.0260.0191.000-0.050-0.0110.0340.0120.0110.002-0.0180.0180.0740.0830.0860.0730.0370.0210.0000.0000.000
angel0.1140.0690.003-0.116-0.019-0.017-0.002-0.0501.0000.004-0.031-0.018-0.0100.004-0.007-0.016-0.037-0.034-0.022-0.019-0.011-0.0040.0000.0000.000
grant0.061-0.009-0.028-0.073-0.012-0.0040.008-0.0110.0041.000-0.0010.001-0.003-0.003-0.002-0.040-0.019-0.005-0.004-0.0010.000-0.0040.0000.0000.000
private_equity0.086-0.107-0.077-0.001-0.016-0.011-0.0030.034-0.031-0.0011.0000.0210.022-0.003-0.007-0.0290.0090.0310.0470.0350.0320.0240.0000.0000.000
post_ipo_equity0.042-0.064-0.043-0.004-0.008-0.002-0.0010.012-0.0180.0010.0211.0000.1080.011-0.005-0.020-0.0100.0000.0090.0070.0080.0080.0090.0180.000
post_ipo_debt0.019-0.039-0.021-0.010-0.004-0.0050.0010.011-0.010-0.0030.0220.1081.000-0.001-0.003-0.016-0.0050.0040.0070.006-0.002-0.0010.0000.0240.000
secondary_market0.019-0.014-0.0040.011-0.002-0.003-0.0020.0020.004-0.003-0.0030.011-0.0011.000-0.0010.0110.0210.0270.0170.0180.0160.0390.0000.0000.000
product_crowdfunding0.0070.045-0.014-0.037-0.0010.002-0.001-0.018-0.007-0.002-0.007-0.005-0.003-0.0011.000-0.011-0.021-0.016-0.011-0.007-0.004-0.0020.0000.0000.000
round_A0.351-0.119-0.0870.542-0.044-0.032-0.0110.018-0.016-0.040-0.029-0.020-0.0160.011-0.0111.0000.3640.1860.0970.0550.0310.0070.0130.0000.000
round_B0.418-0.213-0.1270.503-0.034-0.021-0.0060.074-0.037-0.0190.009-0.010-0.0050.021-0.0210.3641.0000.4100.2230.1220.0680.0250.0030.0000.000
round_C0.343-0.209-0.1080.390-0.025-0.009-0.0070.083-0.034-0.0050.0310.0000.0040.027-0.0160.1860.4101.0000.4350.2240.1220.0400.0000.0000.000
round_D0.246-0.167-0.0790.270-0.017-0.010-0.0050.086-0.022-0.0040.0470.0090.0070.017-0.0110.0970.2230.4351.0000.3940.1980.0740.0000.0000.117
round_E0.160-0.116-0.0530.175-0.011-0.0040.0000.073-0.019-0.0010.0350.0070.0060.018-0.0070.0550.1220.2240.3941.0000.3800.1420.0000.0000.510
round_F0.098-0.068-0.0320.102-0.0060.004-0.0030.037-0.0110.0000.0320.008-0.0020.016-0.0040.0310.0680.1220.1980.3801.0000.3000.0000.0000.166
round_G0.043-0.031-0.0140.045-0.0030.0020.0050.021-0.004-0.0040.0240.008-0.0010.039-0.0020.0070.0250.0400.0740.1420.3001.0000.0000.0000.577
status0.0610.1120.0040.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0130.0030.0000.0000.0000.0000.0001.0000.0910.000
state_code0.0310.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0911.0000.000
round_H0.0790.0000.0000.5050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1170.5100.1660.5770.0000.0001.000

Missing values

2023-07-04T09:30:01.440472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-04T09:30:03.155703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-04T09:30:05.587055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

permalinknamehomepage_urlcategory_listmarketfunding_total_usdstatuscountry_codestate_coderegioncityfunding_roundsfounded_atfounded_monthfounded_quarterfounded_yearfirst_funding_atlast_funding_atseedventureequity_crowdfundingundisclosedconvertible_notedebt_financingangelgrantprivate_equitypost_ipo_equitypost_ipo_debtsecondary_marketproduct_crowdfundinground_Around_Bround_Cround_Dround_Eround_Fround_Ground_H
0/organization/waywire#waywirehttp://www.waywire.com|Entertainment|Politics|Social Media|News|News17,50,000acquiredUSANYNew York CityNew York1.02012-06-012012-062012-Q22012.02012-06-302012-06-301750000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1/organization/tv-communications&TV Communicationshttp://enjoyandtv.com|Games|Games40,00,000operatingUSACALos AngelesLos Angeles2.0NaNNaNNaNNaN2010-06-042010-09-230.04000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
2/organization/rock-your-paper'Rock' Your Paperhttp://www.rockyourpaper.org|Publishing|Education|Publishing40,000operatingESTNaNTallinnTallinn1.02012-10-262012-102012-Q42012.02012-08-092012-08-0940000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3/organization/in-touch-network(In)Touch Networkhttp://www.InTouchNetwork.com|Electronics|Guides|Coffee|Restaurants|Music|iPhone|Apps|Mobile|iOS|E-Commerce|Electronics15,00,000operatingGBRNaNLondonLondon1.02011-04-012011-042011-Q22011.02011-04-012011-04-011500000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
4/organization/r-ranch-and-mine-R- Ranch and MineNaN|Tourism|Entertainment|Games|Tourism60,000operatingUSATXDallasFort Worth2.02014-01-012014-012014-Q12014.02014-08-172014-09-260.00.060000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
5/organization/club-domains.Club Domainshttp://nic.club/|Software|Software70,00,000NaNUSAFLFt. LauderdaleOakland Park1.02011-10-102011-102011-Q42011.02013-05-312013-05-310.07000000.00.00.00.00.00.00.00.00.00.00.00.00.07000000.00.00.00.00.00.00.0
6/organization/fox-networks.Fox Networkshttp://www.dotfox.com|Advertising|Advertising49,12,393closedARGNaNBuenos AiresBuenos Aires1.0NaNNaNNaNNaN2007-01-162007-01-160.00.00.04912393.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
7/organization/0-6-com0-6.comhttp://www.0-6.com|Curated Web|Curated Web20,00,000operatingNaNNaNNaNNaN1.02007-01-012007-012007-Q12007.02008-03-192008-03-190.02000000.00.00.00.00.00.00.00.00.00.00.00.02000000.00.00.00.00.00.00.00.0
8/organization/004-technologies004 Technologieshttp://004gmbh.de/en/004-interact|Software|Software-operatingUSAILSpringfield, IllinoisChampaign1.02010-01-012010-012010-Q12010.02014-07-242014-07-240.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
9/organization/01games-technology01Games Technologyhttp://www.01games.hk/|Games|Games41,250operatingHKGNaNHong KongHong Kong1.0NaNNaNNaNNaN2014-07-012014-07-0141250.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
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54285NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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54292NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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permalinknamehomepage_urlcategory_listmarketfunding_total_usdstatuscountry_codestate_coderegioncityfunding_roundsfounded_atfounded_monthfounded_quarterfounded_yearfirst_funding_atlast_funding_atseedventureequity_crowdfundingundisclosedconvertible_notedebt_financingangelgrantprivate_equitypost_ipo_equitypost_ipo_debtsecondary_marketproduct_crowdfundinground_Around_Bround_Cround_Dround_Eround_Fround_Ground_H# duplicates
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4856